Joint Classification of Hyperspectral and LiDAR Data Based on Position-Channel Cooperative Attention Network

Abstract

Remote sensing image classification is a prominent topic in earth observation research, but there is a performance bottleneck when classifying single-source objects. As the types of remote sensing data are gradually diversified, the joint classification of multi-source remote sensing data becomes possible. However, the existing classification methods have limitations in heterogeneous feature representation of multimodal remote sensing data, which restrict the collaborative classification performance. To resolve this issue, a position-channel collaborative attention network is proposed for the joint classification of hyperspectral and LiDAR data. Firstly, in order to extract the spatial, spectral, and elevation features of land cover objects, a multiscale network and a single-branch backbone network are designed. Then, the proposed position-channel collaborative attention module adaptively enhances the features extracted from the multi-scale network in different degrees through the self-attention module, and exploits the features extracted from the multiscale network and single-branch network through the cross-attention module, so as to capture the comprehensive features of HSI and LiDAR data, narrow the semantic differences of heterogeneous features, and realize complementary advantages. The depth intersection mode further improves the performance of collaborative classification. Finally, a series of comparative experiments were carried out in the 2012 Houston dataset and Trento dataset, and the effectiveness of the model was proved by qualitative and quantitative comparison.