Subpixel Land Cover Mapping Based on Dual Processing Paths for Hyperspectral Image

The subpixel mapping (SPM) technique can handle coarse fractional images derived by unmixing coarse original hyperspectral (HS) image to produce a fine land cover map at the subpixel scale. A popular SPM approach is a two-step model. It first increases the spatial resolution of coarse fractional images by subpixel sharpening to produce fine fractional images and then assigns class labels to each subpixel by the class allocation method. However, there is only a single processing path of the current SPM algorithm, and the information type of the fine fractional images is not rich. To enrich the information type, SPM based on dual processing paths (DPP) is proposed. DPP contains two processing paths, namely spatial-spectral path and multiscale path. First, the coarse original HS image and the high spatial resolution multispectral image are fused by component substitution to produce the fine fractional images with more spatial-spectral information in the spatial-spectral path. At the same time, deep Laplacian pyramid networks are used to obtain the fine fractional images with multiscale information in the multiscale path. The fine fractional images from the two paths are then integrated to generate the improved fraction images with multiscale spatial-spectral information. Finally, the multiscale spatial-spectral information is utilized to allocate class labels by the class allocationmethod. Experimental results on three realHSremote sensing data showthat the proposed DPP outperforms the other SPM methods, demonstrating the effectiveness of the use of DPP in enriching the information type of the fine fractional images.