Multiscale and Multidirection Feature Extraction Network for Hyperspectral and LiDAR Classification

This is the authors' abstract. We don't add key points for this paper.

Deep learning (DL) plays an increasingly important role in earth observation by multi-source remote sensing. However, the current DL-based methods do not make fully use of the complementary information among multi-source remote sensing data, such as hyperspectral image (HSI) and light detection and ranging (LiDAR) data, and lack the consideration of multi-scale, directional and fine-grained features. To address these issues, a multi-scale and multi-direction feature extraction network is proposed in this article. Specifically, multi-scale spatial feature (MSSpaF) module is designed to extract the multi-scale spatial features, and then these features are fused by feature concatenation operation. In addition, multi-direction spatial feature (MDSpaF) module is designed to further extract multi-direction and frequency information, employing cross-layer connection and multi-scale feature fusion strategy to improve fineness of the proposed network. Moreover, spectral feature (SpeF) module is employed to provide detailed spectral information for enhancing the expression ability of multi-scale features. Experimental results on three different datasets demonstrate the superior classification performance of the proposed framework. The source code of this method can be found athttps://github.com/lyywowo/MSMD-Net.