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 (HSIs) with elevation information rmation from light detection and ranging (LiDAR) measurements. In this paper, we propose to fuse the data sets using orthogonal total variation component analysis (OTVCA). Extinction profiles are used to automatically extract spatial and elevation informat information ion from HSI and rasterized LiDAR features. The extracted spatial and elevation information is then fused with spectral information using the OTVCA OTVCA-based based feature fusion method to produce the final classification map. The extracted features have high dimens dimension, ion, and therefore OTVCA estimates the fused features in a lower dimensional space. OTVCA also promotes piece-wise 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) (OTVCA with an urban data set captured over an urban area in Houston/USA and a rural region acquired in Trento/Italy. In the experiments, OTVCA OTVCA-fusion fusion is evaluated using random forest and support vector mac machine hine classifiers. Our experiments demonstrate the ability of OTVCA OTVCA-fusion fusion to produce accurate classification maps