SAR Images Of Areas Research Articles

AUTOMATIC FLOOD DETECTION USING SENTINEL-1 IMAGES IN GOOGLE EARTH ENGINE

The paper presents a method to detect flooded areas based on the classification of superpixels from Sentinel-1 satellite image data. The automatic thresholding method based on the Simple Non-Iterative Clustering and the OTSU algorithm extracts flooded areas from Sentinel-1 images on the Google Earth Engine platform. Experimental results in Quang Binh province in October 2016 show the efficiency of the proposed method (Simple Non-Iterative Clustering algorithm combined with the OTSU algorithm). The proposed method accuracy is 91% higher than the OTSU algorithm, with about 86% in classifying flooded areas in SAR images. The results obtained in the study area can provide timely information about the flooded area, contributing to effective response and reduction of damage caused by floods. The study also shows the power of the Google Earth Engine platform. Google Earth Engine has supported rapid data analysis and processing thanks to rich data sources, multi-time, diverse data, and various algorithms.

Application of a New Semi-Automatic Algorithm for the Detection of Subsidence Areas in SAR Images on the Example of the Upper Silesian Coal Basin

The article presents a new method of automatic detection of subsidence troughs caused by underground coal mining. Land subsidence that results from mining leads to considerable damage to subsurface and surface infrastructure such as walls of buildings, road surfaces, and water relations in built-up areas. Within next 30 years, all coal mines are to be closed as part of the transformation of the mining industry in Poland. However, this is not going to solve the problem of subsidence in those areas. Thus, it is necessary to detect and constantly monitor such hazards. One of the techniques used for that purpose is DInSAR (differential interferometry synthetic aperture radar). It makes it possible to monitor land deformation over large areas with high accuracy and very good spatial and temporal resolution. Subsidence, particularly related to mining, usually manifests itself in interferograms in the form of elliptical interferometric fringes. An important issue here is partial or full automation of the subsidence detection process, as manual analysis is time-consuming and unreliable. Most of the proposed trough detection methods (i.e., Hough transform, circlet transform, circular Gabor filters, template recognition) focus on the shape of the troughs. They fail, however, when the interferometric fringes do not have distinct elliptical shapes or are very noisy. The method presented in this article is based on the analysis of the variability of the phase value in a micro-area of a relatively high entropy. The algorithm was tested for differential interferograms form the Upper Silesian Coal Basin (southern Poland). Due to mining, the studied area is particularly prone to various types of subsidence.

Dark Area Classification From Airborne SAR Images Based on Deep Margin Learning

The dark area in the SAR image corresponds to the zero-energy electromagnetic echo region because specular reflection occurs when the emitted electromagnetic wave (EMW) comes into contact with a smooth object on the ground. In addition, EWM sometimes cannot measure the entire portion of the ground object due to the incident angle of the actual aperture radar sensor and the height of the object on the ground, which creates a dark area in the SAR image called a SAR shadow. Therefore, it is challenging to distinguish the dark area from shadows or some ground objects with smooth surfaces. To address this problem, this paper proposes a novel deep marginal learning algorithm (DMLA) to implement the dark zone classification of airborne SAR images. Specifically, a novel Snake Search Algorithm (SSA) with a chain code is deployed to generate margin slices of dark areas in SAR images, which is used as the input to the following classification network. In addition, a deep convolutional neural network with simple architecture is constructed to train the margin slices of the SSA, which outputs the final classification results with labels. Finally, experiments on three public airborne SAR image datasets output the average classification accuracy of 98.775% for the dark margin slices and an accuracy of 100% to identify dark regions in the original SAR images. The proposed method not only contributes to a high classification accuracy but also improves time efficiency.

Automatic Extraction of Water and Shadow from SAR Images Based on a Multi-Resolution Dense Encoder and Decoder Network.

The water and shadow areas in SAR images contain rich information for various applications, which cannot be extracted automatically and precisely at present. To handle this problem, a new framework called Multi-Resolution Dense Encoder and Decoder (MRDED) network is proposed, which integrates Convolutional Neural Network (CNN), Residual Network (ResNet), Dense Convolutional Network (DenseNet), Global Convolutional Network (GCN), and Convolutional Long Short-Term Memory (ConvLSTM). MRDED contains three parts: the Gray Level Gradient Co-occurrence Matrix (GLGCM), the Encoder network, and the Decoder network. GLGCM is used to extract low-level features, which are further processed by the Encoder. The Encoder network employs ResNet to extract features at different resolutions. There are two components of the Decoder network, namely, the Multi-level Features Extraction and Fusion (MFEF) and Score maps Fusion (SF). We implement two versions of MFEF, named MFEF1 and MFEF2, which generate separate score maps. The difference between them lies in that the Chained Residual Pooling (CRP) module is utilized in MFEF2, while ConvLSTM is adopted in MFEF1 to form the Improved Chained Residual Pooling (ICRP) module as the replacement. The two separate score maps generated by MFEF1 and MFEF2 are fused with different weights to produce the fused score map, which is further handled by the Softmax function to generate the final extraction results for water and shadow areas. To evaluate the proposed framework, MRDED is trained and tested with large SAR images. To further assess the classification performance, a total of eight different classification frameworks are compared with our proposed framework. MRDED outperformed by reaching 80.12% in Pixel Accuracy (PA) and 73.88% in Intersection of Union (IoU) for water, 88% in PA and 77.11% in IoU for shadow, and 95.16% in PA and 90.49% in IoU for background classification, respectively.

Water area extraction using RADARSAT SAR imagery combined with Landsat imagery and terrain information.

This paper exploits an effective water extraction method using SAR imagery in preparation for flood mapping in unpredictable flood situations. The proposed method is based on the thresholding method using SAR amplitude, terrain information, and object-based classification techniques for noise removal. Since the water areas in SAR images have the lowest amplitude value, the thresholding method using SAR amplitude could effectively extract water bodies. However, the reflective properties of water areas in SAR imagery cannot distinguish the occluded areas caused by steep relief and they can be eliminated with terrain information. In spite of the thresholding method using SAR amplitude and terrain information, noises which interfered with users’ interpretation of water maps still remained and the object-based classification using an object size criterion was applied for the noise removal and the criterion was determined by a histogram-based technique. When only using SAR amplitude information, the overall accuracy was 83.67%. However, using SAR amplitude, terrain information and the noise removal technique, the overall classification accuracy over the study area turned out to be 96.42%. In particular, user accuracy was improved by 46.00%.

A cloud-integrated web platform for marine monitoring using GIS and remote sensing. Application to oil spill detection through SAR images

Geographic Information Systems (GIS) have gained popularity in recent years because they provide spatial data management and access through the Web. This article gives a detailed description of a tool that offers an integrated framework for the detection and localization of marine spills using remote sensing, GIS, and cloud computing. Advanced segmentation algorithms are presented in order to isolate dark areas in SAR images, including fuzzy clustering and wavelets. In addition, cloud computing is used for scaling up the algorithms and providing communication between users.

Automatic detection of built-up areas in high-resolution polarimetric SAR images

In this paper a method for automatic detection of built-up areas in high-resolution polarimetric SAR images is presented. A feature-based approach was used. Most of the features are based on statistical properties of built-up areas in SAR images. One feature is based on the isotropic spatial distribution of small uniform regions within built-up areas. Introducing this feature allows one to avoid false alarms due to the presence of edges. The features are fused using logistic regression. Results of applying the method on an L-Band fully polarised SAR image with a spatial resolution of 1.5 m are shown.

A wavelet-based texture feature set applied to classification of multifrequency polarimetric SAR images

Texture is an essential key to the classification of land cover in SAR images. A wavelet-based texture feature set is derived. It consists of the energy of subimages obtained by the overcomplete wavelet decomposition of local areas in SAR images, where the downsampling between wavelet levels is omitted. The feature set has been successfully applied to multifrequency polarimetric images of the Flevoland site, an agricultural area in The Netherlands. The methods of polarization selection and feature reduction are also discussed.