Dual Heterogeneous Network for Hyperspectral Image Classification

Modeling discriminative spectral-spatial features is a key to improving hyperspectral image classification performance. However, existing methods cannot fully characterize the spatial specificity of hyperspectral images, thus making them unable to fully explore the useful information within the image and further improve the discriminative power of features. To address this issue, this work proposes a dual heterogeneous network (DHNet) for hyperspectral image classification. Specifically, the network consists of spatial-specific and spectral-specific branches and captures spectral-spatial features with complementarity by combining convolution and spectral-spatial involution. To better characterize spatial specificity, the spectral-spatial involution modifies the weight parameters based on the center spectral information and neighborhood spatial information of various spatial locations. Besides, two feature calibration modules are proposed. Spatial-specific and spectral-specific weights are generated from the respective branches to calibrate the features captured by the other branches to improve the information interaction between the two branches. The center spectral mapping integrates the spectral features of the target pixel into the feature to suppress the influence of the neighboring disturbing pixels. Experimental results on four datasets indicate that DHNet achieves an accuracy improvement of 1.23%, 2.03%, 2.52%, and 1.77% over the state-of-the-art peers, respectively.