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Understanding the state of changed areas requires that precise information begiven about the changes. Thus, detecting different kinds of changes isimportant for land surface monitoring. SAR sensors are ideal to fulfil thistask, because of their all-time and all-weather capabilities, with goodaccuracy of the acquisition geometry and without effects of atmosphericconstituents for amplitude data. In this study, we propose a simplifiedgeneralized likelihood ratio ($S_{GLR}$) method assuming that correspondingtemporal pixels have the same equivalent number of looks (ENL). Thanks to thedenoised data provided by a ratio-based multitemporal SAR image denoisingmethod (RABASAR), we successfully applied this similarity test approach tocompute the change areas. A new change magnitude index method and an improvedspectral clustering-based change classification method are also developed. Inaddition, we apply the simplified generalized likelihood ratio to detect themaximum change magnitude time, and the change starting and ending times. Then,we propose to use an adaptation of the REACTIV method to visualize thedetection results vividly. The effectiveness of the proposed methods isdemonstrated through the processing of simulated and SAR images, and thecomparison with classical techniques. In particular, numerical experimentsproved that the developed method has good performances in detecting farmlandarea changes, building area changes, harbour area changes and flooding areachanges.

Multitemporal SAR images change detection and visualization using RABASAR and simplified GLR