Silvia Ullo | Università degli Studi del Sannio (original) (raw)

Papers by Silvia Ullo

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

In recent years, machine learning algorithms have become widespread in all the fields of remote s... more In recent years, machine learning algorithms have become widespread in all the fields of remote sensing and earth observation. This has allowed the rapid development of new procedures to solve problems affecting these sectors. In this context, this work aims at presenting a novel method for filtering speckle noise from Sentinel-1 ground range detected data by applying deep learning (DL) algorithms, based on convolutional neural networks. This article provides an easy yet very effective approach to extract the large amount of training data needed for DL approaches in this challenging case. The experimental results on simulated speckled images and an actual synthetic aperture radar dataset show a clear improvement with respect to the state of the art in terms of peak signal-to-noise ratio, structural similarity index, equivalent number of looks, proving the effectiveness of the proposed architecture. Index Terms-Artificial intelligence (AI), convolutional neural networks (CNNs), deep learning (DL), ground range detected (GRD) data, noise filtering, Sentinel-1, speckle filtering, synthetic aperture radar (SAR).

Remote Sensing

Timely information on land use, vegetation coverage, and air and water quality, are crucial for m... more Timely information on land use, vegetation coverage, and air and water quality, are crucial for monitoring and managing territories, especially for areas in which there is dynamic urban expansion. However, getting accessible, accurate, and reliable information is not an easy task, since the significant increase in remote sensing data volume poses challenges for the timely processing and analysis of the resulting massive data volume. From this perspective, classical methods for urban monitoring present some limitations and more innovative technologies, such as artificial-intelligence-based algorithms, must be exploited, together with performing cloud platforms and ad hoc pre-processing steps. To this end, this paper presents an approach to the use of cloud-enabled deep-learning technology for urban sprawl detection and monitoring, through the fusion of optical and synthetic aperture radar data, by integrating the Google Earth Engine cloud platform with deep-learning techniques throug...

In recent years, Machine Learning (ML) algorithms have become widespread in all the fields of Rem... more In recent years, Machine Learning (ML) algorithms have become widespread in all the fields of Remote Sensing (RS) and Earth Observation (EO). This has allowed the rapid development of new procedures to solve problems affecting these sectors. In this context, this work aims at presenting a novel method for filtering speckle noise from Sentinel-1 Ground Range Detected (GRD) data by applying Deep Learning (DL) algorithms, based on Convolutional Neural Networks (CNNs). The paper provides an easy yet very effective approach to extract the large amount of training data needed for DL approaches in this challenging case. The experimental results on simulated speckled images and an actual SAR dataset show a clear improvement with respect to the state of the art in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Equivalent Number of Looks (ENL), proving the effectiveness of the proposed architecture.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021

This article aims to investigate how circuit-based hybrid quantum convolutional neural networks (... more This article aims to investigate how circuit-based hybrid quantum convolutional neural networks (QCNNs) can be successfully employed as image classifiers in the context of remote sensing. The hybrid QCNNs enrich the classical architecture of convolutional neural networks by introducing a quantum layer within a standard neural network. The novel QCNN proposed in this work is applied to the land-use and land-cover classification, chosen as an Earth observation (EO) use case, and tested on the EuroSAT dataset used as the reference benchmark. The results of the multiclass classification prove the effectiveness of the presented approach by demonstrating that the QCNN performances are higher than the classical counterparts. Moreover, investigation of various quantum circuits shows that the ones exploiting quantum entanglement achieve the best classification scores. This study underlines the potentialities of applying quantum computing to an EO case study and provides the theoretical and experimental background for future investigations. Index Terms-Earth observation (EO), image classification, land-use and land-cover (LULC) classification, machine learning (ML), quantum computing (QC), quantum machine learning (QML), remote sensing. I. INTRODUCTION E ARTH observation (EO) has consistently leveraged technological and computational advances helping in developing novel techniques to characterize and model the human environment [1]-[3]. Given that many remote sensing missions are currently operative, carrying on board multispectral, hyperspectral, and radar sensors, and the improved capabilities in transmitting and saving a continuously increasing number of images, nowadays estimated in over 150 terabytes per day [4], the amount of data from EO applications have reached impressive volumes so that they are referred to as Big Data. At the same time, advances in computational technologies and analysis Manuscript

The abundance of clouds, located both spatially and temporally, often makes remote sensing (RS) a... more The abundance of clouds, located both spatially and temporally, often makes remote sensing (RS) applications with optical images difficult or even impossible to perform. Traditional cloud removing techniques have been studied for years, and recently, Machine Learning (ML)-based approaches have also been considered. In this manuscript, a novel method for the restoration of clouds-corrupted optical images is presented, able to generate the whole optical scene of interest, not only the cloudy pixels, and based on a Joint Data Fusion paradigm, where three deep neural networks are hierarchically combined. Spatio-temporal features are separately extracted by a conditional Generative Adversarial Network (cGAN) and by a Convolutional Long Short-Term Memory (ConvLSTM), from Synthetic Aperture Radar (SAR) data and optical time-series of data respectively, and then combined with a U-shaped network. The use of time-series of data has been rarely explored in the state of the art for this peculia...

ArXiv, 2021

The abundance of clouds, located both spatially and temporally, often makes remote sensing applic... more The abundance of clouds, located both spatially and temporally, often makes remote sensing applications with optical images difficult or even impossible. In this manuscript a novel method for clouds-corrupted optical images restoration has been presented and developed, based on a joint data fusion paradigm, where three deep neural networks have been combined in order to fuse spatio-temporal features extracted from Sentinel-1 and Sentinel-2 time-series of data. It is worth to highlight that both the code and the dataset have been implemented from scratch and made available to interested research for further analysis and investigation.

ArXiv, 2021

In recent years, Machine Learning (ML) algorithms have become widespread in all fields of Remote ... more In recent years, Machine Learning (ML) algorithms have become widespread in all fields of Remote Sensing (RS) and Earth Observation (EO). This has allowed a rapid development of new procedures to solve problems affecting these sectors. In this context, the authors of this work aim to present a novel method for filtering the speckle noise from Sentinel-1 data by applying Deep Learning (DL) algorithms, based on Convolutional Neural Networks (CNNs). The obtained results, if compared with the state of the art, show a clear improvement in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), by proving the effectiveness of the proposed architecture. Moreover, the generated open-source code and dataset have been made available for further developments and investigation by interested researchers.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020

In this article, we propose a new procedure to monitor critical infrastructures. The proposed app... more In this article, we propose a new procedure to monitor critical infrastructures. The proposed approach is applied to COSMO-SkyMed data, with the aim to monitor the destabilization of the Mosul dam. Such a dam represents the largest hydraulic facility of Iraq and is located on the Tigris river. The destructive potential of the wave that would be generated, in the event of the dam destruction, could have serious consequences. If the concern for human lives comes first, the concern for cultural heritage protection is not negligible, since several archaeological sites are located around the Mosul dam. The proposed procedure is an in-depth modal assessment based on the micromotion estimation, through a Doppler subapertures tracking and a multichromatic analysis. The method is based initially on the persistent scatterers interferometry that is also discussed for completeness and validation. The modal analysis has detected the presence of several areas of resonance that could mean the presence of cracks, and the results have shown that the dam is still in a strong destabilization. Moreover, the dam appears to be divided into two parts: the northern part is accelerating rapidly while the southern part is decelerating and a main crack in this north-south junction is found. The estimated velocities through the PS-InSAR technique show a good agreement with the GNSS in situ measurements, resulting in a very high correlation coefficient and showing how the proposed procedure works efficiently.

The problem of detecting point like targets over a glistening surface is investigated in this man... more The problem of detecting point like targets over a glistening surface is investigated in this manuscript, and the design of an optimal waveform through a two-step process for a multipath exploitation radar is proposed. In the first step, a non-adaptive waveform is transmitted and a constrained Generalized Likelihood Ratio Test (GLRT) detector is deduced at reception which exploits multipath returns in the range cell under test by modelling the target echo as a superposition of the direct plus the multipath returns. Under the hypothesis of heterogeneous environments, thus by assuming a compound-Gaussian distribution for the clutter return, this latter is estimated in the range cell under test through the secondary data, which are collected from the out-of-bin cells. The Fixed Point Estimate (FPE) algorithm is applied in the clutter estimation, then used to design the adaptive waveform for transmission in the second step of the algorithm, in order to suppress the clutter coming from t...

This paper describes the main features and the objectives of a research project that is in an ini... more This paper describes the main features and the objectives of a research project that is in an initial phase. The project, namely NETCHIP, aims to propose a flexible mobility system based on small and low-emission vehicles (from 4 to 10/12 seats), as a step towards the Mobility as a Service (MaaS) paradigm. The paper summarises the main characteristics of the proposed service from the user viewpoint, the technologic aspects to consider for its implementation, the main challenges to afford and the possible solutions.

ISPRS International Journal of Geo-Information, 2021

The aim of this concept paper is the description of a new tool to support institutions in the imp... more The aim of this concept paper is the description of a new tool to support institutions in the implementation of targeted countermeasures, based on quantitative and multi-scale elements, for the fight and prevention of emergencies, such as the current COVID-19 pandemic. The tool is a cloud-based centralized system; a multi-user platform that relies on artificial intelligence (AI) algorithms for the processing of heterogeneous data, which can produce as an output the level of risk. The model includes a specific neural network which is first trained to learn the correlations between selected inputs, related to the case of interest: environmental variables (chemical–physical, such as meteorological), human activity (such as traffic and crowding), level of pollution (in particular the concentration of particulate matter) and epidemiological variables related to the evolution of the contagion. The tool realized in the first phase of the project will serve later both as a decision support ...

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021

This article aims to explore the potential of current approaches for quantum image classification... more This article aims to explore the potential of current approaches for quantum image classification in the context of remote sensing. After a brief outline of quantum computers and an analysis of the current bottlenecks, it shows for the first time experiments with quantum neural networks on a reference Earth observation (EO) dataset: EuroSAT. Moreover, it establishes the proof of concept of quantum computing for EO: the models trained and run on a quantum simulator are on par with classical ones. We make the open-source code available for further developments 1 .

Remote Sensing, 2021

Modern sensors find their wide usage in a variety of applications such as robotics, navigation, a... more Modern sensors find their wide usage in a variety of applications such as robotics, navigation, automation, remote sensing, underwater imaging, etc. and in recent years the sensors with advanced techniques such as the artificial intelligence (AI) play a significant role in the field of remote sensing and smart agriculture. The AI enabled sensors work as smart sensors and additionally the advent of the Internet of Things (IoT) has resulted into very useful tools in the field of agriculture by making available different types of sensor-based equipment and devices. In this paper, we have focused on an extensive study of the advances in smart sensors and IoT, employed in remote sensing and agriculture applications such as the assessment of weather conditions and soil quality; the crop monitoring; the use of robots for harvesting and weeding; the employment of drones. The emphasis has been given to specific types of sensors and sensor technologies by presenting an extensive study, review...

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019

In this paper, the authors aim to combine the latest state of the art models in image recognition... more In this paper, the authors aim to combine the latest state of the art models in image recognition with the best publicly available satellite images to create a system for landslide risk mitigation. We focus first on landslide detection and further propose a similar system to be used for prediction. Such models are valuable as they could easily be scaled up to provide data for hazard evaluation, as satellite imagery becomes increasingly available. The goal is to use satellite images and correlated data to enrich the public repository of data and guide disaster relief efforts for locating precise areas where landslides have occurred. Different image augmentation methods are used to increase diversity in the chosen dataset and create more robust classification. The resulting outputs are then fed into variants of 3-D convolutional neural networks. A review of the current literature indicates there is no research using CNNs (Convolutional Neural Networks) and freely available satellite imagery for classifying landslide risk. The model has shown to be ultimately able to achieve a significantly better than baseline accuracy.

SoftwareX, 2021

Nowadays the use of Machine Learning (ML) algorithms is spreading in the field of Remote Sensing,... more Nowadays the use of Machine Learning (ML) algorithms is spreading in the field of Remote Sensing, with applications ranging from detection and classification of land use and monitoring to the prediction of many natural or anthropic phenomena of interest. One main limit of their employment is related to the need for a huge amount of data for training the neural network, chosen for the specific application, and the resulting computational weight and time required to collect the necessary data. In this letter the architecture of an innovative tool, enabling researchers to create in an automatic way suitable datasets for AI (Artificial Intelligence) applications in the EO (Earth Observation) context, is presented. Two versions of the architecture have been implemented and made available on Git-Hub, with a specific Graphical User Interface (GUI) for nonexpert users.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021

This article presents a novel method of landslide detection by exploiting the Mask R-CNN capabili... more This article presents a novel method of landslide detection by exploiting the Mask R-CNN capability of identifying an object layout by using a pixel-based segmentation, along with transfer learning used to train the proposed model. A data set of 160 elements is created containing landslide and nonlandslide images. The proposed method consists of three steps: augmenting training image samples to increase the volume of the training data; finetuning with limited image samples; and performance evaluation of the algorithm in terms of precision, recall, and F1 measure, on the considered landslide images, by adopting ResNet-50 and 101 as backbone models. The experimental results are quite encouraging as the proposed method achieves precision equals to 1.00, recall 0.93, and F1 measure 0.97, when ResNet-101 is used as backbone model, and with a low number of landslide photographs used as training samples. The proposed algorithm can be potentially useful for land-use planners and policymakers of hilly areas where intermittent slope deformations necessitate landslide detection as a prerequisite before planning. Index Terms-Convolutional neural networks (CNNS), global positioning system (GPS), landslide detection, Mask R-CNN, region based convolutional neural networks (R-CNN), terrestrial laser scanning (TLS), transfer learning. I. INTRODUCTION L ANDSLIDES or mudslides are an extensive phenomenon, resulting in huge upheavals worldwide with a great frequency [1]-[3]. It is a significant hydro-geological threat affecting large areas of the world, and in particular the India country, including the Western Ghats, Northeastern hill areas, Himalayan regions, etc. The Northwest Himalayan regions of India, incorporating Himachal Pradesh, Jammu & Kashmir and Uttarakhand, are known for highest landslide hazard prone areas. Many heritage temples and Hindu pilgrim sites such as Badrinath, Kedarnath, and Kailash Mansarovar are situated in

Remote Sensing, 2021

In recent years, the growth of Machine Learning (ML) algorithms has raised the number of studies ... more In recent years, the growth of Machine Learning (ML) algorithms has raised the number of studies including their applicability in a variety of different scenarios. Among all, one of the hardest ones is the aerospace, due to its peculiar physical requirements. In this context, a feasibility study, with a prototype of an on board Artificial Intelligence (AI) model, and realistic testing equipment and scenario are presented in this work. As a case study, the detection of volcanic eruptions has been investigated with the objective to swiftly produce alerts and allow immediate interventions. Two Convolutional Neural Networks (CNNs) have been designed and realized from scratch, showing how to efficiently implement them for identifying the eruptions and at the same time adapting their complexity in order to fit on board requirements. The CNNs are then tested with experimental hardware, by means of a drone with a paylod composed of a generic processing unit (Raspberry PI), an AI processing ...

Remote Sensing, 2020

Land-use and land-cover (LULC) classification using remote sensing imagery plays a vital role in ... more Land-use and land-cover (LULC) classification using remote sensing imagery plays a vital role in many environment modeling and land-use inventories. In this study, a hybrid feature optimization algorithm along with a deep learning classifier is proposed to improve the performance of LULC classification, helping to predict wildlife habitat, deteriorating environmental quality, haphazard elements, etc. LULC classification is assessed using Sat 4, Sat 6 and Eurosat datasets. After the selection of remote-sensing images, normalization and histogram equalization methods are used to improve the quality of the images. Then, a hybrid optimization is accomplished by using the local Gabor binary pattern histogram sequence (LGBPHS), the histogram of oriented gradient (HOG) and Haralick texture features, for the feature extraction from the selected images. The benefits of this hybrid optimization are a high discriminative power and invariance to color and grayscale images. Next, a human group-b...

Smart Cities, 2021

Sensors and electronics technologies are pivotal in several fields of science and engineering, es... more Sensors and electronics technologies are pivotal in several fields of science and engineering, especially in automation, industry and environment monitoring. Over the years, there have been continuous changes and advancements in design and miniaturization of sensors with the growth of their application areas. Challenges have arisen in the deployment, fabrication and calibration of modern sensors. Therefore, although the usage of sensors has greatly helped improving the quality of life, especially through their employment in many IoT (Internet of Things) applications, some threats and safety issues still remain unaddressed. In this paper, a brief review focusing on pervasive sensors used for health and indoor environment monitoring is given. Examples of technology advancements in air, water and radioactivity are discussed. This bird’s eye view suggests that solid-state pervasive sensors have become essential parts of all emerging applications related to monitoring of health and safet...

IEEE Access, 2020

Classification of environmental sounds plays a key role in security, investigation, robotics sinc... more Classification of environmental sounds plays a key role in security, investigation, robotics since the study of the sounds present in a specific environment can allow to get significant insights. Lack of standardized methods for an automatic and effective environmental sound classification (ESC) creates a need to be urgently satisfied. As a response to this limitation, in this paper, a hybrid model for automatic and accurate classification of environmental sounds is proposed. Optimum allocation sampling (OAS) is used to elicit the informative samples from each class. The representative samples obtained by OAS are turned into the spectrogram containing their time-frequency-amplitude representation by using a short-time Fourier transform (STFT). The spectrogram is then given as an input to pre-trained AlexNet and Visual Geometry Group (VGG)-16 networks. Multiple deep features are extracted using the pre-trained networks and classified by using multiple classification techniques namely decision tree (fine, medium, coarse kernel), k-nearest neighbor (fine, medium, cosine, cubic, coarse and weighted kernel), support vector machine, linear discriminant analysis, bagged tree and softmax classifiers. The ESC-10, a ten-class environmental sound dataset, is used for the evaluation of the methodology. An accuracy of 90.1%, 95.8%, 94.7%, 87.9%, 95.6%, and 92.4% is obtained with a decision tree, k-neared neighbor, support vector machine, linear discriminant analysis, bagged tree and softmax classifier respectively. The proposed method proved to be robust, effective, and promising in comparison with other existing state-of-the-art techniques, using the same dataset. INDEX TERMS Environmental sound classification, optimal allocation sampling, spectrogram, convolutional neural network, classification techniques.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

In recent years, machine learning algorithms have become widespread in all the fields of remote s... more In recent years, machine learning algorithms have become widespread in all the fields of remote sensing and earth observation. This has allowed the rapid development of new procedures to solve problems affecting these sectors. In this context, this work aims at presenting a novel method for filtering speckle noise from Sentinel-1 ground range detected data by applying deep learning (DL) algorithms, based on convolutional neural networks. This article provides an easy yet very effective approach to extract the large amount of training data needed for DL approaches in this challenging case. The experimental results on simulated speckled images and an actual synthetic aperture radar dataset show a clear improvement with respect to the state of the art in terms of peak signal-to-noise ratio, structural similarity index, equivalent number of looks, proving the effectiveness of the proposed architecture. Index Terms-Artificial intelligence (AI), convolutional neural networks (CNNs), deep learning (DL), ground range detected (GRD) data, noise filtering, Sentinel-1, speckle filtering, synthetic aperture radar (SAR).

Remote Sensing

Timely information on land use, vegetation coverage, and air and water quality, are crucial for m... more Timely information on land use, vegetation coverage, and air and water quality, are crucial for monitoring and managing territories, especially for areas in which there is dynamic urban expansion. However, getting accessible, accurate, and reliable information is not an easy task, since the significant increase in remote sensing data volume poses challenges for the timely processing and analysis of the resulting massive data volume. From this perspective, classical methods for urban monitoring present some limitations and more innovative technologies, such as artificial-intelligence-based algorithms, must be exploited, together with performing cloud platforms and ad hoc pre-processing steps. To this end, this paper presents an approach to the use of cloud-enabled deep-learning technology for urban sprawl detection and monitoring, through the fusion of optical and synthetic aperture radar data, by integrating the Google Earth Engine cloud platform with deep-learning techniques throug...

In recent years, Machine Learning (ML) algorithms have become widespread in all the fields of Rem... more In recent years, Machine Learning (ML) algorithms have become widespread in all the fields of Remote Sensing (RS) and Earth Observation (EO). This has allowed the rapid development of new procedures to solve problems affecting these sectors. In this context, this work aims at presenting a novel method for filtering speckle noise from Sentinel-1 Ground Range Detected (GRD) data by applying Deep Learning (DL) algorithms, based on Convolutional Neural Networks (CNNs). The paper provides an easy yet very effective approach to extract the large amount of training data needed for DL approaches in this challenging case. The experimental results on simulated speckled images and an actual SAR dataset show a clear improvement with respect to the state of the art in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Equivalent Number of Looks (ENL), proving the effectiveness of the proposed architecture.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021

This article aims to investigate how circuit-based hybrid quantum convolutional neural networks (... more This article aims to investigate how circuit-based hybrid quantum convolutional neural networks (QCNNs) can be successfully employed as image classifiers in the context of remote sensing. The hybrid QCNNs enrich the classical architecture of convolutional neural networks by introducing a quantum layer within a standard neural network. The novel QCNN proposed in this work is applied to the land-use and land-cover classification, chosen as an Earth observation (EO) use case, and tested on the EuroSAT dataset used as the reference benchmark. The results of the multiclass classification prove the effectiveness of the presented approach by demonstrating that the QCNN performances are higher than the classical counterparts. Moreover, investigation of various quantum circuits shows that the ones exploiting quantum entanglement achieve the best classification scores. This study underlines the potentialities of applying quantum computing to an EO case study and provides the theoretical and experimental background for future investigations. Index Terms-Earth observation (EO), image classification, land-use and land-cover (LULC) classification, machine learning (ML), quantum computing (QC), quantum machine learning (QML), remote sensing. I. INTRODUCTION E ARTH observation (EO) has consistently leveraged technological and computational advances helping in developing novel techniques to characterize and model the human environment [1]-[3]. Given that many remote sensing missions are currently operative, carrying on board multispectral, hyperspectral, and radar sensors, and the improved capabilities in transmitting and saving a continuously increasing number of images, nowadays estimated in over 150 terabytes per day [4], the amount of data from EO applications have reached impressive volumes so that they are referred to as Big Data. At the same time, advances in computational technologies and analysis Manuscript

The abundance of clouds, located both spatially and temporally, often makes remote sensing (RS) a... more The abundance of clouds, located both spatially and temporally, often makes remote sensing (RS) applications with optical images difficult or even impossible to perform. Traditional cloud removing techniques have been studied for years, and recently, Machine Learning (ML)-based approaches have also been considered. In this manuscript, a novel method for the restoration of clouds-corrupted optical images is presented, able to generate the whole optical scene of interest, not only the cloudy pixels, and based on a Joint Data Fusion paradigm, where three deep neural networks are hierarchically combined. Spatio-temporal features are separately extracted by a conditional Generative Adversarial Network (cGAN) and by a Convolutional Long Short-Term Memory (ConvLSTM), from Synthetic Aperture Radar (SAR) data and optical time-series of data respectively, and then combined with a U-shaped network. The use of time-series of data has been rarely explored in the state of the art for this peculia...

ArXiv, 2021

The abundance of clouds, located both spatially and temporally, often makes remote sensing applic... more The abundance of clouds, located both spatially and temporally, often makes remote sensing applications with optical images difficult or even impossible. In this manuscript a novel method for clouds-corrupted optical images restoration has been presented and developed, based on a joint data fusion paradigm, where three deep neural networks have been combined in order to fuse spatio-temporal features extracted from Sentinel-1 and Sentinel-2 time-series of data. It is worth to highlight that both the code and the dataset have been implemented from scratch and made available to interested research for further analysis and investigation.

ArXiv, 2021

In recent years, Machine Learning (ML) algorithms have become widespread in all fields of Remote ... more In recent years, Machine Learning (ML) algorithms have become widespread in all fields of Remote Sensing (RS) and Earth Observation (EO). This has allowed a rapid development of new procedures to solve problems affecting these sectors. In this context, the authors of this work aim to present a novel method for filtering the speckle noise from Sentinel-1 data by applying Deep Learning (DL) algorithms, based on Convolutional Neural Networks (CNNs). The obtained results, if compared with the state of the art, show a clear improvement in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), by proving the effectiveness of the proposed architecture. Moreover, the generated open-source code and dataset have been made available for further developments and investigation by interested researchers.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020

In this article, we propose a new procedure to monitor critical infrastructures. The proposed app... more In this article, we propose a new procedure to monitor critical infrastructures. The proposed approach is applied to COSMO-SkyMed data, with the aim to monitor the destabilization of the Mosul dam. Such a dam represents the largest hydraulic facility of Iraq and is located on the Tigris river. The destructive potential of the wave that would be generated, in the event of the dam destruction, could have serious consequences. If the concern for human lives comes first, the concern for cultural heritage protection is not negligible, since several archaeological sites are located around the Mosul dam. The proposed procedure is an in-depth modal assessment based on the micromotion estimation, through a Doppler subapertures tracking and a multichromatic analysis. The method is based initially on the persistent scatterers interferometry that is also discussed for completeness and validation. The modal analysis has detected the presence of several areas of resonance that could mean the presence of cracks, and the results have shown that the dam is still in a strong destabilization. Moreover, the dam appears to be divided into two parts: the northern part is accelerating rapidly while the southern part is decelerating and a main crack in this north-south junction is found. The estimated velocities through the PS-InSAR technique show a good agreement with the GNSS in situ measurements, resulting in a very high correlation coefficient and showing how the proposed procedure works efficiently.

The problem of detecting point like targets over a glistening surface is investigated in this man... more The problem of detecting point like targets over a glistening surface is investigated in this manuscript, and the design of an optimal waveform through a two-step process for a multipath exploitation radar is proposed. In the first step, a non-adaptive waveform is transmitted and a constrained Generalized Likelihood Ratio Test (GLRT) detector is deduced at reception which exploits multipath returns in the range cell under test by modelling the target echo as a superposition of the direct plus the multipath returns. Under the hypothesis of heterogeneous environments, thus by assuming a compound-Gaussian distribution for the clutter return, this latter is estimated in the range cell under test through the secondary data, which are collected from the out-of-bin cells. The Fixed Point Estimate (FPE) algorithm is applied in the clutter estimation, then used to design the adaptive waveform for transmission in the second step of the algorithm, in order to suppress the clutter coming from t...

This paper describes the main features and the objectives of a research project that is in an ini... more This paper describes the main features and the objectives of a research project that is in an initial phase. The project, namely NETCHIP, aims to propose a flexible mobility system based on small and low-emission vehicles (from 4 to 10/12 seats), as a step towards the Mobility as a Service (MaaS) paradigm. The paper summarises the main characteristics of the proposed service from the user viewpoint, the technologic aspects to consider for its implementation, the main challenges to afford and the possible solutions.

ISPRS International Journal of Geo-Information, 2021

The aim of this concept paper is the description of a new tool to support institutions in the imp... more The aim of this concept paper is the description of a new tool to support institutions in the implementation of targeted countermeasures, based on quantitative and multi-scale elements, for the fight and prevention of emergencies, such as the current COVID-19 pandemic. The tool is a cloud-based centralized system; a multi-user platform that relies on artificial intelligence (AI) algorithms for the processing of heterogeneous data, which can produce as an output the level of risk. The model includes a specific neural network which is first trained to learn the correlations between selected inputs, related to the case of interest: environmental variables (chemical–physical, such as meteorological), human activity (such as traffic and crowding), level of pollution (in particular the concentration of particulate matter) and epidemiological variables related to the evolution of the contagion. The tool realized in the first phase of the project will serve later both as a decision support ...

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021

This article aims to explore the potential of current approaches for quantum image classification... more This article aims to explore the potential of current approaches for quantum image classification in the context of remote sensing. After a brief outline of quantum computers and an analysis of the current bottlenecks, it shows for the first time experiments with quantum neural networks on a reference Earth observation (EO) dataset: EuroSAT. Moreover, it establishes the proof of concept of quantum computing for EO: the models trained and run on a quantum simulator are on par with classical ones. We make the open-source code available for further developments 1 .

Remote Sensing, 2021

Modern sensors find their wide usage in a variety of applications such as robotics, navigation, a... more Modern sensors find their wide usage in a variety of applications such as robotics, navigation, automation, remote sensing, underwater imaging, etc. and in recent years the sensors with advanced techniques such as the artificial intelligence (AI) play a significant role in the field of remote sensing and smart agriculture. The AI enabled sensors work as smart sensors and additionally the advent of the Internet of Things (IoT) has resulted into very useful tools in the field of agriculture by making available different types of sensor-based equipment and devices. In this paper, we have focused on an extensive study of the advances in smart sensors and IoT, employed in remote sensing and agriculture applications such as the assessment of weather conditions and soil quality; the crop monitoring; the use of robots for harvesting and weeding; the employment of drones. The emphasis has been given to specific types of sensors and sensor technologies by presenting an extensive study, review...

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019

In this paper, the authors aim to combine the latest state of the art models in image recognition... more In this paper, the authors aim to combine the latest state of the art models in image recognition with the best publicly available satellite images to create a system for landslide risk mitigation. We focus first on landslide detection and further propose a similar system to be used for prediction. Such models are valuable as they could easily be scaled up to provide data for hazard evaluation, as satellite imagery becomes increasingly available. The goal is to use satellite images and correlated data to enrich the public repository of data and guide disaster relief efforts for locating precise areas where landslides have occurred. Different image augmentation methods are used to increase diversity in the chosen dataset and create more robust classification. The resulting outputs are then fed into variants of 3-D convolutional neural networks. A review of the current literature indicates there is no research using CNNs (Convolutional Neural Networks) and freely available satellite imagery for classifying landslide risk. The model has shown to be ultimately able to achieve a significantly better than baseline accuracy.

SoftwareX, 2021

Nowadays the use of Machine Learning (ML) algorithms is spreading in the field of Remote Sensing,... more Nowadays the use of Machine Learning (ML) algorithms is spreading in the field of Remote Sensing, with applications ranging from detection and classification of land use and monitoring to the prediction of many natural or anthropic phenomena of interest. One main limit of their employment is related to the need for a huge amount of data for training the neural network, chosen for the specific application, and the resulting computational weight and time required to collect the necessary data. In this letter the architecture of an innovative tool, enabling researchers to create in an automatic way suitable datasets for AI (Artificial Intelligence) applications in the EO (Earth Observation) context, is presented. Two versions of the architecture have been implemented and made available on Git-Hub, with a specific Graphical User Interface (GUI) for nonexpert users.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021

This article presents a novel method of landslide detection by exploiting the Mask R-CNN capabili... more This article presents a novel method of landslide detection by exploiting the Mask R-CNN capability of identifying an object layout by using a pixel-based segmentation, along with transfer learning used to train the proposed model. A data set of 160 elements is created containing landslide and nonlandslide images. The proposed method consists of three steps: augmenting training image samples to increase the volume of the training data; finetuning with limited image samples; and performance evaluation of the algorithm in terms of precision, recall, and F1 measure, on the considered landslide images, by adopting ResNet-50 and 101 as backbone models. The experimental results are quite encouraging as the proposed method achieves precision equals to 1.00, recall 0.93, and F1 measure 0.97, when ResNet-101 is used as backbone model, and with a low number of landslide photographs used as training samples. The proposed algorithm can be potentially useful for land-use planners and policymakers of hilly areas where intermittent slope deformations necessitate landslide detection as a prerequisite before planning. Index Terms-Convolutional neural networks (CNNS), global positioning system (GPS), landslide detection, Mask R-CNN, region based convolutional neural networks (R-CNN), terrestrial laser scanning (TLS), transfer learning. I. INTRODUCTION L ANDSLIDES or mudslides are an extensive phenomenon, resulting in huge upheavals worldwide with a great frequency [1]-[3]. It is a significant hydro-geological threat affecting large areas of the world, and in particular the India country, including the Western Ghats, Northeastern hill areas, Himalayan regions, etc. The Northwest Himalayan regions of India, incorporating Himachal Pradesh, Jammu & Kashmir and Uttarakhand, are known for highest landslide hazard prone areas. Many heritage temples and Hindu pilgrim sites such as Badrinath, Kedarnath, and Kailash Mansarovar are situated in

Remote Sensing, 2021

In recent years, the growth of Machine Learning (ML) algorithms has raised the number of studies ... more In recent years, the growth of Machine Learning (ML) algorithms has raised the number of studies including their applicability in a variety of different scenarios. Among all, one of the hardest ones is the aerospace, due to its peculiar physical requirements. In this context, a feasibility study, with a prototype of an on board Artificial Intelligence (AI) model, and realistic testing equipment and scenario are presented in this work. As a case study, the detection of volcanic eruptions has been investigated with the objective to swiftly produce alerts and allow immediate interventions. Two Convolutional Neural Networks (CNNs) have been designed and realized from scratch, showing how to efficiently implement them for identifying the eruptions and at the same time adapting their complexity in order to fit on board requirements. The CNNs are then tested with experimental hardware, by means of a drone with a paylod composed of a generic processing unit (Raspberry PI), an AI processing ...

Remote Sensing, 2020

Land-use and land-cover (LULC) classification using remote sensing imagery plays a vital role in ... more Land-use and land-cover (LULC) classification using remote sensing imagery plays a vital role in many environment modeling and land-use inventories. In this study, a hybrid feature optimization algorithm along with a deep learning classifier is proposed to improve the performance of LULC classification, helping to predict wildlife habitat, deteriorating environmental quality, haphazard elements, etc. LULC classification is assessed using Sat 4, Sat 6 and Eurosat datasets. After the selection of remote-sensing images, normalization and histogram equalization methods are used to improve the quality of the images. Then, a hybrid optimization is accomplished by using the local Gabor binary pattern histogram sequence (LGBPHS), the histogram of oriented gradient (HOG) and Haralick texture features, for the feature extraction from the selected images. The benefits of this hybrid optimization are a high discriminative power and invariance to color and grayscale images. Next, a human group-b...

Smart Cities, 2021

Sensors and electronics technologies are pivotal in several fields of science and engineering, es... more Sensors and electronics technologies are pivotal in several fields of science and engineering, especially in automation, industry and environment monitoring. Over the years, there have been continuous changes and advancements in design and miniaturization of sensors with the growth of their application areas. Challenges have arisen in the deployment, fabrication and calibration of modern sensors. Therefore, although the usage of sensors has greatly helped improving the quality of life, especially through their employment in many IoT (Internet of Things) applications, some threats and safety issues still remain unaddressed. In this paper, a brief review focusing on pervasive sensors used for health and indoor environment monitoring is given. Examples of technology advancements in air, water and radioactivity are discussed. This bird’s eye view suggests that solid-state pervasive sensors have become essential parts of all emerging applications related to monitoring of health and safet...

IEEE Access, 2020

Classification of environmental sounds plays a key role in security, investigation, robotics sinc... more Classification of environmental sounds plays a key role in security, investigation, robotics since the study of the sounds present in a specific environment can allow to get significant insights. Lack of standardized methods for an automatic and effective environmental sound classification (ESC) creates a need to be urgently satisfied. As a response to this limitation, in this paper, a hybrid model for automatic and accurate classification of environmental sounds is proposed. Optimum allocation sampling (OAS) is used to elicit the informative samples from each class. The representative samples obtained by OAS are turned into the spectrogram containing their time-frequency-amplitude representation by using a short-time Fourier transform (STFT). The spectrogram is then given as an input to pre-trained AlexNet and Visual Geometry Group (VGG)-16 networks. Multiple deep features are extracted using the pre-trained networks and classified by using multiple classification techniques namely decision tree (fine, medium, coarse kernel), k-nearest neighbor (fine, medium, cosine, cubic, coarse and weighted kernel), support vector machine, linear discriminant analysis, bagged tree and softmax classifiers. The ESC-10, a ten-class environmental sound dataset, is used for the evaluation of the methodology. An accuracy of 90.1%, 95.8%, 94.7%, 87.9%, 95.6%, and 92.4% is obtained with a decision tree, k-neared neighbor, support vector machine, linear discriminant analysis, bagged tree and softmax classifier respectively. The proposed method proved to be robust, effective, and promising in comparison with other existing state-of-the-art techniques, using the same dataset. INDEX TERMS Environmental sound classification, optimal allocation sampling, spectrogram, convolutional neural network, classification techniques.