Assessment of Combining Convolutional Neural Networks and Object Based Image Analysis to Land Cover Classification Using Sentinel 2 Satellite Imagery (Tenes Region, Algeria) (original) (raw)
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Springer in series Lecture Notes in Artificial Intelligence LNCS/LNAI, 2019
Regional land use planning and monitoring remain an issue in many developing countries. Efficient solution for both tasks depended on remote sensing technology to capture and analyze remotely sensed data of the region of interest. Although a plethora of methods for land cover classification have been reported, the problem remained a challenging task in computer vision field. The advent of deep learning method in the past decade has been very instrumental to develop a robust method for land cover classification using satellite imagery as input. The objective of this paper was to present empiric results on using CNN as a land cover classifier model using Sentinel-2 spatial satellite imagery. Prior to model training, the input image representation was extracted using eCognition to produce texture, brightness, shape, and vegetation index. Land cover labeling followed the Land Cover Class in Medium Resolution Optical Imagery Interpretation document provided by Indonesian National Standardization Agency. The training of CNN model achieved 0.98 mean training accuracy and 0.98 mean testing accuracy. As comparison, the same data and same feature were trained with another model: Gradient Boosting Model (GBM). The results revealed that the training accuracy and testing accuracy with GBMs were 0.98 and 0.95 respectively. CNN model showed small improvement of the accuracy to classify land cover with the image feature (NDVI, Brightness, GLCM homogeneity and Rectangular fit).
Remote Sensing
Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential for many environmental and social applications. The increase in availability of RS data has led to the development of new techniques for digital pattern classification. Very recently, deep learning (DL) models have emerged as a powerful solution to approach many machine learning (ML) problems. In particular, convolutional neural networks (CNNs) are currently the state of the art for many image classification tasks. While there exist several promising proposals on the application of CNNs to LULC classification, the validation framework proposed for the comparison of different methods could be improved with the use of a standard validation procedure for ML based on cross-validation and its subsequent statistical analysis. In this paper, we propose a general CNN, with a fixed architecture and parametrization, to achieve high accuracy on LULC classification over RS data from different sources such as...
DEEP LEARNING FOR LAND COVER MAPPING USING SENTINEL-2 IMAGERY: A CASE STUDY AT GREATER CAIRO, EGYPT
IEEE International Geoscience and Remote Sensing Symposium, 2023
Land cover mapping is essential for various applications, and the integration of satellite imagery and deep learning techniques offers an accurate and efficient solution. This study focuses on mapping land cover and change detection in the new extensions of Greater Cairo, Egypt, using Sentinel-2 imagery and convolutional neural networks (CNNs). The CNN model was trained on the BigEarthNet dataset, and transfer learning was applied using a pre-trained U-Net model. The results reveal significant land cover changes in Greater Cairo, particularly in the eastern region due to the construction of the New Administrative Capital. The accuracy assessment metrics, including precision, recall, and F1-score, demonstrate high accuracy levels exceeding 90%. These findings contribute to the advancement of land cover mapping and its applications in urban development.
Deep learning approach for land use images classification
E3S Web of Conferences
CNN (convolutional neural networks) are a category of neural networks that are majorly used for image classification and recognition. This Deep Learning (DL) technique is used to solve complex problems, particularly for environmental protection, its approaches have affected several domains without exception, geospatial world is one vised domain. In this paper we aim to classify aerial images of Tangier region, city located in north of Morocco, by using pixel based image classification with convolutional Neural Networks. Flickr API is used to get our test images dataset. These images are used as input to a pretrained network Resnet18, a small convolution neural network architecture, which is able to recognize 21 land use classes of images. Our methodology is based on the following steps, first we set up the data, and then we re-train the cited Deep Learning model (Transfer Learning) and perform a quick and visual verification, by generating a labeled map from the geotagged images, la...
Application of Deep Learning in Satellite Image-based Land Cover Mapping in Africa
International Journal of Advanced Computer Science and Applications
Deep Learning Networks (DLN), in particular, Convolutional Neural Networks (CNN) has achieved state-of-theart results in various computer vision tasks including automatic land cover classification from satellite images. However, despite its remarkable performance and broad use in developed countries, using this advanced machine learning algorithm has remained a huge challenge in developing continents such as Africa. This is because the necessary tools, techniques, and technical skills needed to utilize DL networks are very scarce or expensive. Recently, new approaches to satellite image-based land cover classification with DL have yielded significant breakthroughs, offering novel opportunities for its further development and application. This can be taken advantage of in low resources continents such as Africa. This paper aims to review some of these notable challenges to the application of DL for satellite image-based classification tasks in developing continents. Then, review the emerging solutions as well as the prospects of their use. Harnessing the power of satellite data and deep learning for land cover mapping will help many of the developing continents make informed policies and decisions to address some of its most pressing challenges including urban and regional planning, environmental protection and management, agricultural development, forest management and disaster and risks mitigation.
Satellite-Net: Automatic Extraction of Land Cover Indicators from Satellite Imagery by Deep Learning
ArXiv, 2019
In this paper we address the challenge of land cover classification for satellite images via Deep Learning (DL). Land Cover aims to detect the physical characteristics of the territory and estimate the percentage of land occupied by a certain category of entities: vegetation, residential buildings, industrial areas, forest areas, rivers, lakes, etc. DL is a new paradigm for Big Data analytics and in particular for Computer Vision. The application of DL in images classification for land cover purposes has a great potential owing to the high degree of automation and computing performance. In particular, the invention of Convolution Neural Networks (CNNs) was a fundament for the advancements in this field. In [1], the Satellite Task Team of the UN Global Working Group describes the results achieved so far with respect to the use of earth observation for Official Statistics. However, in that study, CNNs have not yet been explored for automatic classification of imagery. This work invest...
International Journal of Advanced Computer Science and Applications, 2022
Deep Learning algorithms have become more popular in computer vision, especially in the image classification field. This last has many applications such as moving object detection, cancer detection, and the classification of satellite images, also called images of land use-land cover (LULC), which are the scope of this paper. It represents the most commonly used method for decision making in the sustainable management of natural resources at various geographical levels. However, methods of satellite images analysis are expensive in the computational time and did not show good performance. Therefore, this paper, on the one hand, proposes a new CNN architecture called Modified MobileNet V1 (MMN) based on the fusion of MobileNet V1 and ResNet50. On the other hand, it presents a comparative study of the proposed model and the most used models based on transfer learning, i.e. MobileNet V1, VGG16, DenseNet201, and ResNet50. The experiments were conducted on the dataset Eurosat, and they show that ResNet50 results emulate the other models.
Land use Land Cover Classifıcation using Deep Learning Classifiers for Remote Sensed Images
International Conference on IoT based Control Networks and Intelligent Systems (ICICNIS 2020), 2020
The image classification accuracy is enhanced by applying Deep Learn ing Models (DLM) which has a robus t learning ability by incorporating both feature extract ion and classification p rocedure into single image classificat ion test. Here a deep learn in g-based classification technique is applied to High Spatial Resolution Remote Sensing Images (HSRRSI) to ext ract mult i-layer features. The two networks i.e., residual network and inception network are co mb ined into one new model to obtain higher accuracy then said individual residual network and inception network. The new model designed was extensively weighed on data's from Remote Sensing Image Classificat ion Benchmark (RSI-CB). The dataset obtained from RSI-CB is split into 70:30 rat io for training and testing respectively. The performances of proposed approach are then assessed by kappa coefficient (K) and accuracy (A).
(IJACSA) International Journal of Advanced Computer Science and Applications, 2021
Deep Learning Networks (DLN), in particular, Convolutional Neural Networks (CNN) has achieved state-of-theart results in various computer vision tasks including automatic land cover classification from satellite images. However, despite its remarkable performance and broad use in developed countries, using this advanced machine learning algorithm has remained a huge challenge in developing continents such as Africa. This is because the necessary tools, techniques, and technical skills needed to utilize DL networks are very scarce or expensive. Recently, new approaches to satellite image-based land cover classification with DL have yielded significant breakthroughs, offering novel opportunities for its further development and application. This can be taken advantage of in low resources continents such as Africa. This paper aims to review some of these notable challenges to the application of DL for satellite image-based classification tasks in developing continents. Then, review the emerging solutions as well as the prospects of their use. Harnessing the power of satellite data and deep learning for land cover mapping will help many of the developing continents make informed policies and decisions to address some of its most pressing challenges including urban and regional planning, environmental protection and management, agricultural development, forest management and disaster and risks mitigation.
Fastai and Convolutional Neural Network Based Land Cover Classification
E3S Web of Conferences
The primary objective of this research is to create a Deep Learning model that can accurately classify satellite images into predefined categories. To accomplish this goal, we developed an effective approach for satellite image classification that utilizes deep learning and the convolutional neural network (CNN) for feature extraction. We trained our model using a labeled dataset of satellite images provided by Planet Labs, which specializes in detecting various types of land covers. By utilizing the CNN algorithm, we were able to automatically extract features from satellite data with relatively minimal processing compared to other image classification algorithms. To develop our model, we employed the Fastai library, which enables us to quickly and effortlessly achieve state-of-the-art results in image classification tasks.