Multiview Hierarchical Network for Hyperspectral and LiDAR Data Classification

Abstract

In recent times, multisource remote sensing technology [e.g., hyperspectral image (HSI) and light detection and ranging (LiDAR) data] has been widely used in urban land-use recognition owing to its high classification effectiveness compared to using only single-source data. In this study, a multiview hierarchical network (MVHN) technique is developed for HSI and LiDAR data classification, which conducts the following execution procedures. First, based on the a preset band step length, the original HSI is sampled and divided into multiple groups with exactly the same number of bands to obtain spectral features. Then, principal components analysis is performed on the raw HSI to extract the first principal components (PCs) that meet the size of the LiDAR image. The Gabor filters are applied to the PCs and LiDAR to capture spatial details (i.e., textural features) of scenes. Specifically, a stacking mechanism is employed to generate fusion features once the above features are available. Next, a three-dimensional ResNet-like deep CNN is designed to extract the spectral–spatial information of the fusion feature. Finally, majority-voting is introduced into the classification results of the network trained using each fusion feature to achieve high-confidence final results. Experiments on three well-known HSI and LiDAR datasets (i.e., Houston, MUUFL, and Trento datasets) demonstrate the effectiveness of the proposed MVHN method compared to state-of-the-art comparable classification methods.

Keywords

• Classification
• Gabor feature
• hyperspectral image (HSI)
• light detection and ranging (LiDAR)
• multisource remote sensing
• residual network