A Multi-Level Encoder-Decoder Attention Network for Change Detection in Hyperspectral Images

Convolutional neural networks (CNNs) have attracted much attention in change detection for their superior feature learning ability. However, most of the existing CNN-based change detection methods adopt an early-fusion or late-fusion strategy to fuse low-level spatial details or high-level semantic information. So far, the impact of multi-level fusion strategy across multitemporal hyperspectral (HS) images, and its application to change detection, remains unexplored. In this paper, we propose a multi-level encoder-decoder attention network (ML-EDAN), which allows the network to make full use of the hierarchical features for change detection in HS images. A two-stream encoder-decoder framework is taken as the backbone to exploit and fuse the hierarchical features from all the convolutional layers of multitemporal HS images. Within the encoder-decoder, a contextual-information-guided attention module is developed to yield more effective spatial-spectral feature transfer in the network. After fully obtaining the multi-level hierarchical features, the long short-term memory (LSTM) subnetwork is devised to analyze temporal dependence between multitemporal images. Moreover, the proposed ML-EDAN is trained in an end-to-end manner with a new joint loss function considering both reconstruction error and pixel-wise classification error. The experiments are conducted on three data sets, demonstrating the effectiveness of the proposed ML-EDAN in HS change detection in comparison with widely-accepted state-of-the-art methods.
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