Sparsity-Driven Change Detection in Multitemporal SAR Images

作者：Nar Fatih^*; Ozgur Atilla; Saran Ayse Nurdan

来源：IEEE Geoscience and Remote Sensing Letters, 2016, 13(7): 1032-1036.

DOI：10.1109/LGRS.2016.2562032

• Change detection
• image analysis
• log ratio
• synthetic aperture radar (SAR)
• total variation (TV)
• l(1)-norm

In this letter, a method for detecting changes in multitemporal synthetic aperture radar (SAR) images by minimizing a novel cost function is proposed. This cost function is constructed with log-ratio-based data fidelity terms and an l(1)-norm-based total variation (TV) regularization term. Log-ratio terms model the changes between the two SAR images where the TV regularization term imposes smoothness on these changes in a sparse manner such that fine details are extracted while effects like speckle noise are reduced. The proposed method, sparsity-driven change detection (SDCD), employs accurate approximation techniques for the minimization of the cost function since data fidelity terms are not convex and the employed l(1)-norm TV regularization term is not differentiable. The performance of the SDCD is shown on real-world SAR images obtained from various SAR sensors.

• 出版日期2016-7