Attention Multibranch Convolutional Neural Network for Hyperspectral Image Classification Based on Adaptive Region Search

Abstract

Convolutional neural networks (CNNs) have demonstrated outstanding performance on image classification. To classify the hyperspectral images (HSIs), existing CNN-based approaches commonly adopt the architecture using single or several fixed spatial windows as inputs. This kind of architecture may lose contextual information or incorporate heterogeneous information due to the neglect of various land-cover distributions in HSIs. To deal with this problem, a novel attention multibranch CNN method based on adaptive region search (RS-AMCNN) is proposed for HSI classification. In RS-AMCNN, sizes and locations of spatial windows are searched in the nonlocal candidate region adaptively according to sample-specific distribution. These flexible spatial windows are input into several branches of RS-AMCNN. In each branch, convolutional long short-term memories (ConvLSTMs) are merged into CNN from shallow to deep layers, which not only extracts joint spatial-spectral features, but also exploits complementary information among different layers. Then, a branch attention mechanism is devised to emphasize more discriminative branches and suppress less useful ones. It forces RS-AMCNN to extract multiscale and multicontextual attention features for classification. Finally, RS-AMCNN is optimized end-to-end by combining the losses from the ramose classifiers of different branches and the main classifier. Experiments carried on several benchmark HSI data sets demonstrate that RS-AMCNN provides promising classification performance, especially in edge preservation and region uniformity.