Haoming He
ABSTRACT
Deep Convolutional Neural Networks have been adopted for remote sensing change detection that focused on how to migrate semantic segmentation networks designed for a single image to remote sensing change detection tasks. These networks tend to have an accuracy of the large region rather than boundary quality and small region quality. In this paper, we propose a novel architecture, Siamese Change Detection Network (SCD-Net), and a new hybrid loss, Multi-Scale Perceptual (MSP) Loss, for bi-temporal remote sensing change detection. Specifically, the architecture is composed of a densely supervised Encoder-Decoder network in which, unlike existing work, we add an up-sampling path to the encoder in charge of building multi-level strong semantic feature maps. In this way, the comparison of low-level feature maps is based on global information prior instead of only local information. The Multi-Scale Perceptual (MSP) Loss consists of Tversky loss and a variant of Focal loss. It is applied to the output results of the network at different scales to be able to learn the changed regions at different scales effectively. Equipped with MSP loss, the proposed SCD-Net can effectively segment the change regions and accurately predict the fine structures with accurate boundaries. Experimental results on two public datasets show that our method outperforms the state-of-the-art methods in F1 score.
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