Multiview Feature Learning and Multilevel Information Fusion for Joint Classification of Hyperspectral and LiDAR Data

With the rapid development of earth observation technology, the fusion of multisource remote sensing (RS) data has become an important research field in land cover classification. Particularly, the fusion of hyperspectral image (HSI) and light detection and ranging (LiDAR) data, which can provide complementary information to each other, has attracted an increasing attention for land cover classification tasks. However, most of the existing methods focus on multisource information fusion on feature level and ignore the discrepancy of the land covers with the same label among different kinds of viewpoints, which limit the further improvement of classification performance. So, this article proposes a novel HSI-LiDAR classification method based on multiview feature learning (MVFL) and multilevel information fusion (MLIF), which consists of a multiview data representation (MVDR) strategy, a multibranch dual-channel graph convolutional networks (MB-DCGCNs) model, and a progressively high-confidence label assignment (PHCLA) scheme. MVDR is designed by jointly utilizing multiple attributes of spatial information and multisource RS data to re-express multisource land covers with diversity and complementarity. The MB-DCGCNs model, which aims at integrating spectral-spatial-elevation information on feature level, is employed on the above multiview re-expressed HSI-LiDAR (MVR-HL) data to explore land cover feature representations from different views. Furthermore, a PHCLA scheme is proposed to classify the land covers with high-reliability by a combination of decision-level label prediction and pixel-level label assignment. Comparative experiments on two benchmark datasets with several state-of-the-art classification methods validate the effectiveness and superior performance of the proposed method.