Densely Connected Multiscale Attention Network for Hyperspectral Image Classification

Abstract

Hyperspectral images (HSIs) are characterized by high spatial resolution and are rich in spectral information. In the process of HSI classification, the extraction of spectral-spatial features directly influences the classification results. In recent years, the hyperspectral classification method based on convolutional neural networks has demonstrated excellent performance. However, as the network structure deepens, degradation occurs, and the features learned from the fixed-scale convolutional kernels are usually specific, which is not conducive to feature learning and thus impairs the classification accuracy. To solve the problem of difficult extraction of features and underutilization of information from HSI data, a densely connected multiscale attention network based on 3-D convolution is proposed for HSI classification. First, to reduce the spectral redundancy of the HSIs, the principal component analysis algorithm is performed on the raw HSI data; then, several multiscale blocks comprised of parallel factorized spatial-spectral convolution modules of different sizes are adopted to extract the enriched spectral-spatial features from HSIs; furthermore, dense connections are introduced to further fuse features obtained from blocks of different depths, thereby enhancing feature reuse and propagation and helping to alleviate the problem of vanishing gradients. Besides, the channel-spectral-spatial attention block is put forward to spontaneously reweight the fused features to emphasize the features that are more relevant to the classification results while weakening the less relevant ones. The experimental results show that the proposed method is effective in extracting discriminative features of the target and outperforms the other state-of-the-art methods.