Abstract

Accurate and timely change detection of Earth's surface features is extremely important for understanding relationships and interactions between people and natural phenomena. Post-Classification Comparison (PCC) methods based on supervised change detection are widely used in change detection for remote sensing images, but are easily affected by a significant cumulative error of single remote sensing image classification. Unsupervised change detection methods are affected by the speckle noise and cannot explicitly identify the types of land cover or land use transitions. To solve those problems, this paper proposes a change detection method based on similarity measure and joint classification. The similarity measure is obtained by test statistic and Kittler and Illingworth minimum-error thresholding algorithm (TSKI), which is used to automatically control the joint-classification classifier. The efficiency of the proposed method is demonstrated by the polarimetric synthetic aperture radar (PolSAR) images acquired by Radarsat-2 over Wuhan of China. The experimental results show that the method can identify different types of land cover changes and reduce the false alarms in the change detection.

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