Subpixel Mapping Based on Multisource Remote Sensing Fusion Data for Land-Cover Classes

Subpixel mapping (SPM) based on multisource remote sensing fusion data (MRSFD) for land-cover classes, called SPM-MRSFD, is proposed in this letter. First, the original hyperspectral image and the auxiliary panchromatic image are fused to produce the high spatial and spectral resolution fused image by pan-sharpening technology. Second, the fused image with spatial-spectral information and the auxiliary digital surface model (DSM) of light detection and ranging (LiDAR) with elevation information are fused to obtain the MRSFD with spatial-spectral elevation information by feature fusion. Finally, the fractional images with the proportions of subpixels belonging to land-cover classes are derived by unmixing the MRSFD, and the classes labels are allocated to subpixels to obtain the final SPM result according to these proportions' information. The main contribution of this work is that multiple types of auxiliary information (i.e., spatial-spectral elevation information) from MRSFD is fully utilized and the accuracy of SPM result is improved. Experimental results show that SPM-MRSFD obtains more accurate mapping results than state-of-the-art SPM methods.