Abstract
Subpixel mapping (SPM) algorithms effectively estimate the spatial distribution of different land cover classes within mixed pixels. This paper proposed a new subpixel mapping method based on image structural self-similarity learning. Image structure self-similarity refers to similar structures within the same scale or different scales in image itself or its downsampled image, which widely exists in remote sensing images. Based on the similarity of image block structure, the proposed method estimates higher spatial distribution of coarse-resolution fraction images and realizes subpixel mapping. The experimental results show that the proposed method is more accurate than existing fast subpixel mapping algorithms.
Highlights
IntroductionA sort of soft classification technique, solves the problem of mixed pixels
Mixed pixels widely exist in remote sensing imageries
Significant promotion can be found for slender river restoration, and isolated, scattered point feature mapping results have been obviously improved compared with traditional methods
Summary
A sort of soft classification technique, solves the problem of mixed pixels This method obtains the relative abundance (i.e., the proportion or the component) of each land cover class within each pixel and gets fraction images of each class of hyperspectral remote sensing image. While it is the defect of subpixel unmixing that this technique can only obtain the proportion of each land cover class, it is hard to specify spatial distribution of different land cover classes within pixel, which means that many specific spatial details are still missing. This technique segments mixed pixels into subpixels by appropriate scale in order to predict the class of each subpixel and obtain specific land cover information on higher spatial resolution
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