DSHFNet: Dynamic Scale Hierarchical Fusion Network Based on Multiattention for Hyperspectral Image and LiDAR Data Classification

With the continuous improvement of satellite sensor performance, it is becoming easier to obtain different types of remote sensing (RS) data from multiple sensors, and the fusion of hyperspectral (HS) images and light detection and ranging (LiDAR) for land use/land cover classification has become a research hotspot. However, the current mainstream methods still have defects in feature extraction and feature fusion. In the feature extraction stage, previous methods usually use a single-scale patch as input and a fixed convolution kernel for feature extraction, which makes it difficult to extract features in line with different land cover types at the same time and to obtain high-quality features. Although multi-scale feature extraction can solve the one-sidedness problem of single-scale features, it also brings the challenge of high-dimensional multi-scale features. In the feature fusion stage, the current fusion methods are relatively simple. Therefore, we propose a dynamic scale hierarchical fusion network (DSHFNet) for fusion classification of HS images and LiDAR data. By calculating the similarity in the scale space and judging the information at different scales through the threshold value, the appropriate scale features are dynamically selected, the small-scale features are integrated into the large-scale features, and the dimensionality of the features is reduced. This method solves the unreliability problem of single-scale features and the high dimension problem of multi-scale features. In the feature fusion process, different attention modules are used for hierarchical fusion, spatial attention modules are used for shallow fusion and joint feature extraction, and modal attention modules are used for deep fusion of joint features and features from different sensors to achieve complete complementarity of features. Experimental evaluations on three real RS datasets demonstrate the superiority of the proposed method compared to existing methods. The source code can be downloaded at https://github.com/SYFYN0317/DSHFNet.