Hybrid Constraints of Pure and Mixed Pixels for Soft-Then-Hard Super-Resolution Mapping with Multiple Shifted Images

Multiple shifted images (MSIs) have been widely applied to many super-resolution mapping (SRM) approaches to improve the accuracy of fine-scale land-cover maps. Most SRM methods with MSIs involve two processes: subpixel sharpening and class allocation. Complementary information from the MSIs has been successfully adopted to produce soft attribute values of subpixels during the subpixel sharpening process. Such information, however, is not used in the second process of class allocation. In this paper, a new class-allocation algorithm, named 'hybrid constraints of pure and mixed pixels' (HCPMP), is proposed to allocate land-cover classes to subpixels using MSIs. HCPMP first determines the classes of subpixels that overlap with the pure pixels of auxiliary images in MSIs, after which the remaining subpixels are classified using information derived from the mixed pixels of the base image in MSIs. An artificial image and two remote sensing images were used to evaluate the performance of the proposed HCPMP algorithm. The experimental results demonstrate that HCPMP successfully applied MSIs to produce SRM maps that are visually closer to the reference images and that have greater accuracy than five existing class-allocation algorithms. Especially, it can produce more accurate SRM maps for high-resolution land-cover classes than low-resolution cases. The algorithm takes slightly less runtime than class allocation using linear optimization techniques. Hence, HCPMP provides a valuable new solution for class allocation in SRM using auxiliary data from MSIs.