Superresolution Land-Cover Mapping Based on High-Accuracy Surface Modeling

A new superresolution mapping (SRM) method based on high-accuracy surface modeling (HASM) is proposed to generate land-cover maps at the subpixel scale. HASM uses the fundamental theorem of surfaces to uniquely define a land surface, which can produce less errors in interpolation results than classic methods, and thus, the proposed SRM method first uses it to estimate the soft class values of subpixels according to the fraction images of soft classification. Then, it transforms the soft class values into a hard-classified land-cover map using class allocation under the constraints of fraction images. Experiments on a synthetic image and a real remote sensing image show that the proposed method produces more accurate SRM maps than four existing SRM methods. Hence, the proposed method provides a new option for superresolution land-cover mapping.