Hyperspectral and LiDAR Data Fusion Classification Using Superpixel Segmentation-Based Local Pixel Neighborhood Preserving Embedding

Abstract

A new method of superpixel segmentation-based local pixel neighborhood preserving embedding (SSLPNPE) is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR) data based on the extinction profiles (EPs), superpixel segmentation and local pixel neighborhood preserving embedding (LPNPE). A new workflow is proposed to calibrate the Goddard’s LiDAR, hyperspectral and thermal (G-LiHT) data, which allows our method to be applied to actual data. Specifically, EP features are extracted from both sources. Then, the derived features of each source are fused by the SSLPNPE. Using the labeled samples, the final label assignment is produced by a classifier. For the open standard experimental data and the actual data, experimental results prove that the proposed method is fast and effective in hyperspectral and LiDAR data fusion.

Keywords

• hyperspectral
• light detection and ranging (LiDAR)
• image fusion
• extinction profile (EP)
• superpixel segmentation
• local pixel neighborhood preserving embedding (LPNPE)
• Goddard’s LiDAR
• hyperspectral and thermal (G-LiHT) data