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JBE, vol. 26, no. 1, pp.14-25, January, 2021
Change Attention based Dense Siamese Network for Remote Sensing Change Detection
Gisu Hwang, Woo-Ju Lee, and Seoung-Jun Oh
C.A E-mail: email@example.com
Change detection, which finds changes in remote sensing images of the same location captured at different times, is very important because it is used in various applications. However, registration errors, building displacement errors, and shadow errors cause false positives. To solve these problems, we propose a novle deep convolutional network called CADNet (Change Attention Dense Siamese Network). CADNet uses FPN (Feature Pyramid Network) to detect multi-scale changes, applies a Change Attention Module that attends to the changes, and uses DenseNet as a feature extractor to use feature maps that contain both low-level and high-level features for change detection. CADNet performance measured from the Precision, Recall, F1 side is 98.44%, 98.47%, 98.46% for WHU datasets and 90.72%, 91.89%, 91.30% for LEVIR-CD datasets. The results of this experiment show that CADNet can offer better performance than any other traditional change detection method.
Keyword: Change Detection, Remote Sensing Image, Siamese Network, Attention Mechanism, Densely Connected Convolutional Network
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