Abstract
Abstract. Monitoring and surveillance changes around the world need powerful methods, so detection, visualization, and assessment of significant changes are essential for planning and management. Incorporating polarimetric SAR images due to interactions between electromagnetic waves and target and because of the high spatial resolution almost one meter can be used to study changes in the Earth's surface. Full polarized radar images comparing to single polarized radar images use amplitude and phase information of the surface in different available polarization (HH, HV, VH, and VV). This study is based on the decomposition of full polarized airborne UAVSAR images and integration of these features with algebra method involves Image Differencing (ID) and Image Ratio (IR) algorithms with the mathematical nature and distance-based method involves Canberra (CA) and Euclidean (ED) algorithms with measuring distance between corresponding vector and similarity-based method involves Taminoto (TA) and Kulczynski (KU) algorithms with dependence corresponding vector for change detecting purposes on two real PolSAR datasets. Assessment of incorporated methods is implemented using ground truth data and different criteria for evaluating such as overall accuracy (OA), area under ROC curve (AUC) and false alarms rate (FAR). The output results show that ID, IR, and CA have superiority to detect changes comparing to other implemented algorithms. Also, numerical results show that the highest performance in two datasets has OA more than 90%. In other assessment criteria, mention algorithms have low FAR and high AUC value indices to detect changes in PolSAR images.
Highlights
In recent years with the continuous development of technology and resolution on SAR images have been expansively makes to use this system is more practical (Liu et al, 2012b)
This paper presents match-base methods for land cover change detection using PolSAR images
Based on the number of pixels specified in the error matrix, the overall accuracy (OA), and false alarms rate is defined by the following relationships: TP + TN
Summary
In recent years with the continuous development of technology and resolution on SAR images have been expansively makes to use this system is more practical (Liu et al, 2012b). Developing SAR imaging system, make it possible to use this system in various remote sensing applications such as land use/cover classification, monitoring of urban growth, forest monitoring, and disaster management (Zhong et al, 2015). PolSAR1 images due to the interaction between electromagnetic waves and objects and having the phase and amplitude due to a different scattering mechanisms (surface, double-bounce, and volume scattering) have extra different information from the ground in different polarization (HH, HV, VH, and VV). These images because of the interaction of electromagnetic waves and objects at ground level include an inherent speckle noise (Lê et al, 2015). Change detection has three important step that the first step is image processing which includes co-registration and reduction speckle noise, the second step is producing change map between multitemporal images based on different extracted features and implemented different change detection methods and the third
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