Multiscale Fusion Network Based on Global Weighting for Hyperspectral Feature Selection.

Feature selection (FS) is an important way to achieve high-precision and efficient classification of hyperspectral remote sensing images. However, most existing FS methods use a fixed scale to extract features and the relationship between spatial and spectral dimensions is ignored. In fact, this correlation is useful for classification. In this article, a multiscale feature fusion network based on global weighting (MSFGW) is proposed in which a global weighting mechanism is explored to catch spatial–spectral information at multiple scales. First, the multiscale feature extraction module composed of group convolution and dilated convolution is utilized to extract the multiscale features. With the increase of the dilation rate, the module takes the spatial differences at varying scales. Second, a 3-D weighting mechanism is used to combine the spatial and spectral correlated information for reducing the interference of homologous and heterologous and boosting the feature discrimination ability. Then, multiscale weighted features are fused to integrate the internal information of all bands at different scales. Finally, the band reconstruction network is used to select representative bands according to their entropy. The experimental results with the state-of-the-art FS algorithms on four widely hyperspectral datasets demonstrate that the features selected by MSFGW have obvious advantages in classification with only a few training samples.