Adaptive Feature Exchange Network with Complementary Advantages for Cooperative Classification of Hyperspectral and Multispectral Imagery

With the development of remote sensing technology for earth observation, the collaborative utilization of Hyperspectral images (HSI) and multispectral images (MSI) has received increasing attention in terrestrial observation. HSI and MSI represent two typical types of optical remote sensing data and can provide rich complementary information. However, the paradoxical problem of high spatial resolution and high spectral resolution leads to difficulties in extracting complementary information. In this paper, we propose an Adaptive Feature Exchange network with Complementary Advantages (AFECAnet). Specifically, we enhance the discriminative feature extraction of HSI-MSI by introducing a spectral-spatial feature enhancement module based on a dual attention mechanism. Subsequently, in order to reduce redundant information, we design an adaptive feature interaction strategy based on batch normalization privatization factors. This strategy helps to accurately replace redundant information and reduces the computational burden on the network. Experimental results demonstrate that the proposed AFECAnet has a significant improvement in HSI-MSI collaborative classification.