Multi-superpixelization-based convex formulation for joint classification of hyperspectral and lidar data
The synergistic analysis of light detection and ranging (LiDAR) and hyperspectral data is attracting a significant interest in recent years due to the complementary nature of these two sources of remote sensing data. In this paper, we propose a new spectral-spatial classification method able to jointly exploit these two kinds of data. Our work is based on three innovative components: 1) a superpixel generation method aimed at multivariate image spatial partitioning, 2) a multi-source framework for feature extraction, and 3) a convex framework used to approach the solutions of the resulted image labeling problem associated with vectorial total variation and superpixel-based graph total variation regularizers. Our experimental results, conducted with a hyperspectral data set collected by the Compact Airborne Spectrographic Imager (CASI) spectrometer over the city of Houston in 2013 and a corresponding LiDAR data set, illustrate the effectiveness of the proposed framework.