Robust Unsupervised Small Area Change Detection from SAR Imagery Using Deep Learning

• URL: http://arxiv.org/abs/2011.11005v1
• Date: Sun, 22 Nov 2020 12:50:08 GMT
• Title: Robust Unsupervised Small Area Change Detection from SAR Imagery Using Deep Learning
• Authors: Xinzheng Zhang, Hang Su, Ce Zhang, Xiaowei Gu, Xiaoheng Tan, Peter M. Atkinson
• Abstract summary: A robust unsupervised approach is proposed for small area change detection from synthetic aperture radar (SAR) images. A multi-scale superpixel reconstruction method is developed to generate a difference image (DI) A two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes.
• Score: 23.203687716051697
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Small area change detection from synthetic aperture radar (SAR) is a highly challenging task. In this paper, a robust unsupervised approach is proposed for small area change detection from multi-temporal SAR images using deep learning. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes with a parallel clustering strategy. Image patches belonging to the first two classes are then constructed as pseudo-label training samples, and image patches of the intermediate class are treated as testing samples. Finally, a convolutional wavelet neural network (CWNN) is designed and trained to classify testing samples into changed or unchanged classes, coupled with a deep convolutional generative adversarial network (DCGAN) to increase the number of changed class within the pseudo-label training samples. Numerical experiments on four real SAR datasets demonstrate the validity and robustness of the proposed approach, achieving up to 99.61% accuracy for small area change detection.

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