Adaptive spectral-spatial feature fusion network for hyperspectral image classification using limited training samples

Recently, the excellent power of spectral-spatial feature representation of convolutional neural network (CNN) has gained widespread attention for hyperspectral image (HSI) classification. Nevertheless, the practical performance of CNN-based models in HSI classification is ordinarily limited by the available amount of the training samples. In this article, we investigate the limitations of current CNN-based methods for HSI feature (spectral and spatial) extraction and utilization. For spectral features, the distant inter-band relationships are often neglected. Therefore, a novel spectral band non-localization (SBNL) operation is proposed to enable the non-local spectral inter-band correlations to be excavated by convolutional kernels with limited receptive fields. For spatial features, the extracted spatial multiscale features conventionally isolated in different channels. Subsequently, we develop a novel multiscale-share inception block (MSIB) to exploit the cross-relationships among the multiscale features. More significantly, to better take advantage of the complementary information of spectral and spatial features, a plug-and-play adaptive feature fusion (AFF) module is introduced. Eventually, the adaptive spectral spatial feature fusion network (AS(2)F(2)N) is introduced for HSI classification. Experimental results derived from three benchmark data sets exhibit that the proposed method outperforms previous state-of-the-art CNN-based methods under limited training samples situation. The codes of this work will be available at https://github. com/zhonghaocheng/ELSEVIER_IJAEOG_AS2F2N for the sake of reproducibility.