ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-3-2022
https://doi.org/10.5194/isprs-annals-V-3-2022-665-2022
https://doi.org/10.5194/isprs-annals-V-3-2022-665-2022
17 May 2022
 | 17 May 2022

UNSUPERVISED SAR CHANGE DETECTION METHOD BASED ON REFINED SAMPLE SELECTION

Y. Peng, Z. Wei, and B. Cui

Keywords: Refined sample selection, SAR change detection, enhanced difference image, multi-hierarchical fuzzy C-means, convolutional neural network

Abstract. In deep learning based synthetic aperture radar (SAR) change detection, selecting samples of high quality is a crucial step. In this work, we have proposed a refined sample selection algorithm for unsupervised SAR change detection. The propose and incorporation of volume control factors and multi-hierarchical fuzzy c-means (MH-FCM) algorithm generate samples of large diversity and high confidence, thus satisfying the needs for high quality samples. The method includes two phases: firstly, an enhanced difference image is constructed according to the difference consistency between single pixels and their neighbourhoods, and a triangular threshold segmentation method is then proposed to determine the volume control factors for sample selection. MH-FCM is developed to classify the log mean ratio difference image into 4 classes. Secondly, a dual-channel convolution neural network with an adaptive weighted loss is adopted to learn and predict the input and to obtain the change detection result. Experimental results of the Gaofen-3 dataset in Beijing have validated the effectiveness and usefulness of the proposed method.