Discriminative Feature Extraction and Fusion for Classification of Hyperspectral and Lidar Data

Multisource remote sensing data provide the abundant and complementary information for land cover classification. In this paper, we propose a deep hashing-based feature extraction and fusion framework for joint classification of hyper-spectral and LiDAR data. Firstly, HSIs and LiDAR data are fed into a two-stream network to extract deep features after data preprocessing. Then, we adopt hashing technique to constrain single-source and cross-source similarities, i.e., samples with same classes should have small feature distance and samples with different classes should have large feature distance. Furthermore, a feature-level fusion strategy is exploited to fuse the two kind of multisource information. Finally, we design an object function to consider the similarity information between sample pairs and semantic information of each sample, which can deliver the discriminative features for classification. The experiments on Houston data demonstrate the effectiveness of the proposed method over some competitive approaches.