Producing Subpixel Resolution Thematic Map From Coarse Imagery: MAP Algorithm-Based Super-Resolution Recovery

摘要

Subpixel mapping (SPM) of hyperspectral remote sensing imagery is a promising technique for deriving fine mapping result by classification at fine spatial resolution. There is a type of algorithm for SPM, namely, the soft-then-hard SPM (STHSPM) algorithm that first estimates soft attribute values for land cover classes at subpixel level and then allocates classes for subpixel according to the soft attribute values. However, the fraction images derived from spectral unmixing are of less prior information of original hyperspectral remote sensing imagery and there are lots of errors in SPM result due to the limitation of spectral unmixing technology currently available. In this paper, a framework based on subpixel resolution thematic map, namely, super-resolution then classification (STC) is proposed to improve mapping result. In the proposed framework, a maximum a posteriori (MAP) model associated with the endmembers of interest (EOI), namely, T-MAP-SR is applied to the original coarse imagery to derive a high-resolution imagery with generous prior information. Then fine mapping result can be derived from the high-spatial resolution imagery by the available classification methods. Experiments show that the proposed framework can produce higher mapping accuracy result and protect the classes of interest (COI).