Shadow-Less Intrinsic Hyperspectral Point Cloud Generation From HSIs and LiDAR

Abstract

Generating hyperspectral point cloud from hyperspectral images (HSIs) and Light Detection and Ranging (LiDAR) has become more and more common in the remote sensing field and supported various applications. One challenge here is that hyperspectral imaging is a passive imaging method and is suffering from shadows in a natural scene. Intrinsic information recovery can effectively eliminate the spectral variation caused by illumination changes; however, it assumes a uniform light and neglects the shadows in the scene. In this paper, we provide a novel hyperspectral point cloud intrinsic model that can detect the shaded regions and recover reflectance information in them. We first estimate the global illumination of the scene using an intrinsic information recovery method. Then, we perform supervoxel segmentation on hyperspectral point cloud to calculate the blocking relation of supervoxels and therefore accurately detect shaded regions. Finally, we estimate the illumination and reflectance of shaded regions based on an illumination invariant spectral prior. The experimental results show that the proposed method can effectively detect shaded areas and robustly generate shadow-less intrinsic hyperspectral point cloud.