Sub-Pixel Mapping of Spectral Imagery Based on Deviation Information Measurement

Abstract

Sub-pixel mapping (SPM) technology can analyze mixed pixels in spectral image and realize the transformation from abundance images to a fine sub-pixel classification image. Since the SPM belongs to an ill-posed issue, deviation information (DI) inevitably is in optimal abundance images. Due to simple structure and good robustness, most SPM approaches are according to the spatial dependence assumption; namely, the closer the spatial distance is, the more likely the subpixels belong to the same class. However, the existing SPM methods based on spatial dependence assumption cannot accurately measure the DI, which affects the accuracy of the final mapping result. To address this problem, the SPM based on DI measurement (DIM) is proposed in this work. The DIM uses a dual-scale spatial attraction model (DSAM) to process the coarse abundance images to obtain predicted abundance images. The fine prior images captured at different times from the same field of view are used to measure the deviation abundance images with the DI using a geographically weighted regression (GWR) model. The predicted abundance images and deviation abundance images are fused to derive optimal abundance images. Based on the proportion information on sub-pixels being classes in the optimal abundance images, the class labels are assigned to sub-pixels by label allocation method, yielding the final mapping result. The proposed DIM method is tested on National Land-cover Dataset, Rome Dataset, and Bastrop Fires dataset. The experimental results verify that the proposed DIM achieves the best performance with the overall accuracy of 97.26%, 88.15%, and 99.80% in the three experimental results.