SFFGL: A Semantic Feature Fused Global Learning Framework for Multiclass Change Detection in Hyperspectral Images

Deep learning techniques have shown increasing potential in change detection (CD) in hyperspectral images (HSIs). However, most deep learning-based existing methods for HSI CD follow a patch-based local learning framework and concentrate on binary CD. In this letter, we propose an end-to end semantic feature fused global learning (SFFGL) framework for HSI multiclass CD (MCD). In SFFGL, a global spatialwise fully convolutional network (FCN), which introduces a spatial attention mechanism (PAM) between encoder and decoder, is designed to effectively exploit the global spatial information from the whole HSIs and achieve patch-free inference. PAM can adaptively extract global spatialwise feature representation. In the model training stage, a global hierarchical (GH) sampling strategy is introduced to obtain diverse gradients during back propagation for more robust performance. The semantic-spatial feature fusion (S2F2) unit is designed to effectively fuse the enhanced spatial context information in the encoder and the semantic information in the decoder. More importantly, a semantic feature enhancement module (SEM) is proposed to weaken the influence of the unchanged regional background on the change regional foreground, thus further improving the accuracy. Experimental results on two benchmark HSI datasets demonstrate the effectiveness of the proposed SFFGL.