Hyperspectral and LiDAR Fusion Using Extinction Profiles and Total Variation Component Analysis

Abstract

The classification accuracy of remote sensing data can be increased by integrating ancillary data provided by multisource acquisition of the same scene. We propose to merge the spectral and spatial content of hyperspectral images (HSI) with elevation information from LIDAR measurements. In the present paper, we propose to fuse the datasets using Orthogonal Total Variation Component Analysis (OTVCA). Extinction profiles (EPs) are used to automatically extract spatial and Elevation information from HSI and rasterized LiDAR features. The extracted spatial and elevation information are then fused with spectral information using the OTVCA-based feature fusion method to produce the final classification map. The extracted features have high dimension and therefore OTVCA estimates the fused features in a lower dimensional space. OTVCA also promotes piece-wise smoothness while maintaining the spatial structures. Both attributes are important to provide homogeneous regions in the final classification maps. We benchmark the proposed approach (OTVCA-fusion) with an urban datasets captured over an urban area in Houston/USA and a rural region acquired in Trento/Italy. In the experiments, OTVCA-fusion is evaluated using random forest (RF) and support vector machines (SVM) classifiers. Our experiments demonstrate the ability of OTVCA-fusion to produce accurate classification maps while using fewer features compared to other approaches investigated in this study.