Joint Feature Extraction for Multi-Source Data Using Similar Double-Concentrated Network

Abstract Joint classification of multi-source data is often better than single-source data in application scenes, but it is difficult to ensure effective feature extraction of multi-source information. In this paper, a similar double-concentrate network, denoted as SDCN, is proposed for extracting features effectively and classifying more accurately based on hyperspectral imagery (HSI) and Light Detection and Ranging (LiDAR) data. Specifically, the dual-concentrate network is first developed to capture spectral and spatial features from HSI, and then expanding the connection of LiDAR information based on the trained HSI branch. Each branch of the designed network is similar, which includes two convolutional layers, one maximum pooling layer, one batch normalization layer and two activation layers. After the network of HSI is fully trained, similar network is deployed to distinguish spatial features and ‘band’ difference of LiDAR data, and different features are also combined with multi-source associations. The absolute symmetry network structure and specific multi-source connection can ensure the orderly and balance of features extracted in this model, and adjust the direction of feature extraction constantly. Experimental results on several real data demonstrate that the proposed SDCN outperforms other relevant state-of-the-art methods.