Multitemporal SAR images change detection and visualization using
  RABASAR and simplified GLR
Weiying Zhao Charles-Alban Deledalle Loïc Denis Henri Maître Jean-Marie Nicolas Florence Tupin
Understanding the state of changed areas requires that precise information be
given about the changes. Thus, detecting different kinds of changes is
important for land surface monitoring. SAR sensors are ideal to fulfil this
task, because of their all-time and all-weather capabilities, with good
accuracy of the acquisition geometry and without effects of atmospheric
constituents for amplitude data. In this study, we propose a simplified
generalized likelihood ratio ($S_{GLR}$) method assuming that corresponding
temporal pixels have the same equivalent number of looks (ENL). Thanks to the
denoised data provided by a ratio-based multitemporal SAR image denoising
method (RABASAR), we successfully applied this similarity test approach to
compute the change areas. A new change magnitude index method and an improved
spectral clustering-based change classification method are also developed. In
addition, we apply the simplified generalized likelihood ratio to detect the
maximum change magnitude time, and the change starting and ending times. Then,
we propose to use an adaptation of the REACTIV method to visualize the
detection results vividly. The effectiveness of the proposed methods is
demonstrated through the processing of simulated and SAR images, and the
comparison with classical techniques. In particular, numerical experiments
proved that the developed method has good performances in detecting farmland
area changes, building area changes, harbour area changes and flooding area
changes.