ECDSA-Based Water Bodies Prediction from Satellite Images with UNet (original) (raw)
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Deep Learning for Automatic Extraction of Water Bodies Using Satellite Imagery
Journal of the Indian Society of Remote Sensing
The study introduces an automated approach for extracting water bodies from satellite images using the Faster R-CNN algorithm. The approach was tested on two datasets consisting of water body images collected from Sentinel-2 and Landsat-8 (OLI) satellite images, totaling over 3500 images. The results showed that the proposed approach achieved an accuracy of 98.7% and 96.1% for the two datasets, respectively. This is significantly higher than the accuracy achieved by the convolutional neural network (CNN) approach, which achieved 96% and 80% for the two datasets, respectively. These findings highlight the effectiveness of the proposed approach in accurately mapping water bodies from satellite imagery. Additionally, the Sentinel-2 dataset performed better than the Landsat dataset in both the Faster R-CNN and CNN approaches for water body extraction.
Extracting surface water bodies from sentinel-2 imagery using convolutional neural networks
2021
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesWater is an integral part of eco-system with significant role in human life. It is immensely mobilized natural resource and hence it should be monitored continuously. Water features extracted from satellite images can be utilized for urban planning, disaster management, geospatial dataset update and similar other applications. In this research, surface water features from Sentinel-2 (S2) images were extracted using state-of-the-art approaches of deep learning. Performance of three proposed networks from different research were assessed along with baseline model. In addition, two existing but novel architects of Convolutional Neural Network (CNN) namely; Densely Convolutional Network (DenseNet) and Residual Attention Network (AttResNet) were also implemented to make comparative study of all the networks. Then dense blocks, transition blocks, attention block a...
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International Journal of Advance Research, Ideas and Innovations in Technology, 2019
The rapid growth in satellite imagery has helped scientists understand the Earth better. The improved understanding of the Earth makes it possible for scientists to perform better in all activities that range from disaster management in the form of mobilizing resources to comprehend global warming by monitoring its effects. The major limitation of this achievement is the assumption that significant features in satellite images, like buildings, roads, trees, or water bodies, can be easily identified, either manually or semi-automatically, but always perfectly. In this paper to overcome this limitation, we use different convolutional neural networks with modifications such as proposed PSPNet, U-net architecture, Inverted pyramid and XGBoost algorithm for accurately detecting specified features in satellite images from Defense Science and Technology Laboratory (DSTL) database. Automation of feature detection in satellite images is not only useful in making smart and quick decisions, bu...
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Water bodies on the Earth’s surface are an important part of the hydrological cycle. The water resources of the Kerch Peninsula at this moment can be described as a network with temporary streams and small rivers that dry up in summer. Partially, they are often used in fisheries. But since permanent field monitoring is quite financially and resource-intensive, it becomes necessary to find a way for the automated remote monitoring of water bodies using remote sensing data. In this work, we used remote sensing data obtained using the Sentinel-2 satellite in the period from 2017 to 2022 during the days of field expeditions to map the water bodies of the Kerch Peninsula. As a training data set for surface water prediction, field expeditions data were used. The area for test data collection is located near Lake Tobechikskoye, where there are five water bodies. The Keras framework, written in Python, was used to build the architecture of a deep neural network. The architecture of the neur...
Satellite Imagery Classification with Deep Learning : A Survey
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2020
Object detection from satellite images has been a challenging problem for many years. With the development of effective deep learning algorithms and advancement in hardware systems, higher accuracies have been achieved in the detection of various objects from very high-resolution satellite images. In the past decades satellite imagery has been used successfully for weather forecasting, geographical and geological applications. Low resolution satellite images are sufficient for these sorts of applications. But the technological developments in the field of satellite imaging provide high resolution sensors which expands its field of application. Thus, the High-Resolution Satellite Imagery (HRSI) proved to be a suitable alternative to aerial photogrammetric data to provide a new data source for object detection. Since the traffic rates in developing countries are enormously increasing, vehicle detection from satellite data will be a better choice for automating such systems. In this research, a different technique for vehicle detection from the images obtained from high resolution sensors is reviewed. This review presents the recent progress in the field of object detection from satellite imagery using deep learning.
Journal of Archaeological Science, 2024
Qanats are a remarkable type of ancient hydraulic structure for sustainable water distribution in arid environments that use subterranean channels to transport water from highland or mountainous areas. The presence of the qanat system is marked by a line of regularly spaced shafts visible from the surface, which can be used to detect qanats using satellite imagery. Typically, qanats have been documented by field mapping or manual digitisation within a Geographic Information System (GIS) environment. This process is time-consuming due to the numerous shafts within each qanat line. However, several automated methods for detecting qanat structures have been explored, using techniques such as morphological filters, custom convolutional neural networks (CNN) and, more recently, YOLOv5 and Mask R-CNN. These approaches used high-resolution RGB images and CORONA images. However, the use of black and white CORONA in CNNs has been limited in its applicability due to a high rate of false positives. This paper explores the potential of YOLOv9 in processing the black and white HEXAGON (KH-9) highresolution spy satellite system launched in 1971. Two areas in Afghanistan (Maiwand) and Iran (Gorgan Plain) were selected to train the system images extracted from HEXAGON imagery and artificial synthetic data. The training dataset was augmented using the Albumentation library, which increased the number of tiles used. The model was tested using two types of HEXAGON imagery for selected areas in Afghanistan (Maiwand), Iran (Gorgan Plain) and Morocco (Rissani), and CORONA imagery in Iran (Gorgan Plain). Our study provided a model capable of predicting the location of qanat shafts with a precision of over 0.881 and a recall of 0.627 for most of the case studies tested. This is the first case study aimed at detecting qanats in different landscapes using different types of satellite imagery. Using real, augmented, and artificial data allowed us to generalise the representation of qanats into lineal groups of circular features. Thanks to applying labelling for individual qanats and their pairs as separate classes, our approach eliminated most of the isolated and clustered false positives.
Satellite Image Classification: From Handcrafted Features to Deep Learning Features
Indian Journal of Computer Science and Engineering, 2021
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Impact of data augmentation on identifying water bodies in satellite images
AGRIVOLTAICS2021 CONFERENCE: Connecting Agrivoltaics Worldwide
Recognizing surface water is helpful in an assortment of remote sensing applications, the applications such as assessing the accessibility of water, estimating its adjustment of time, and anticipating dry seasons and floods. However, identifying surface water with old-style techniques is certainly not an essential endeavour. Instead, distant identifying with expansive consideration and distinctive passing noticing is the most intelligent response for surface water checking. To Identify surface water efficiently, we developed a deep learning model that can process full Landsat Image inside a single shot without separating the contribution to tiles and increasing the model accuracy by applying data augmentation resulting in the model coming over a modified version of the original data in every case by using the data augmentation to the images and their identical masks.
Deep learning-based object recognition in multispectral satellite imagery for real-time applications
Machine Vision and Applications
Satellite imagery is changing the way we understand and predict economic activity in the world. Advancements in satellite hardware and low-cost rocket launches have enabled near-real-time, high-resolution images covering the entire Earth. It is too labour-intensive, time-consuming and expensive for human annotators to analyse petabytes of satellite imagery manually. Current computer vision research exploring this problem still lack accuracy and prediction speed, both significantly important metrics for latency-sensitive automatized industrial applications. Here we address both of these challenges by proposing a set of improvements to the object recognition model design, training and complexity regularisation, applicable to a range of neural networks. Furthermore, we propose a fully convolutional neural network (FCN) architecture optimised for accurate and accelerated object recognition in multispectral satellite imagery. We show that our FCN exceeds human-level performance with state-of-the-art 97.67% accuracy over multiple sensors, it is able to generalize across dispersed scenery and outperforms other proposed methods to date. Its computationally light architecture delivers a fivefold improvement in training time and a rapid prediction, essential to real-time applications. To illustrate practical model effectiveness, we analyse it in algorithmic trading environment. Additionally, we publish a proprietary annotated satellite imagery dataset for further development in this research field. Our findings can be readily implemented for other real-time applications too.