Fusion of Hyperspectral and Lidar Images Using Non-Subsampled Shearlettransform

Abstract

In this paper, a new fusion method for merging the spectral and spatial contents of hyperspectral images (HSI) with the height information of light detection and ranging (LiDAR) for increasing the classification accuracy of HSI is introduced. First, 2D non-subsampled shearlet transform (NSST) is applied to each band of hyperspectral and LiDAR data separately in order to extract the spatial features. Second, principal component analysis (PCA) is applied to all shearlet subbands of HSI in order to reduce their dimension. Third, the spectral information of HSI and obtained spatial features are integrated and classified using subspace multinomial logistic regression (MLRsub). We evaluate the performance of the proposed method over University of Houston, USA and a rural one captured over Trento, Italy. The obtained results show that the proposed method can efficiently classify the joint hyperspectral and LiDAR images.