Clonal-selection-based method for positioning subpixel of high spectrum remote sensing image

Abstract

The invention provides a clonal-selection-based method for positioning a subpixel of a high spectrum remote sensing image. Subpixel positioning of the high spectrum remote sensing image is realized on the basis of a clonal theory and a subpixel positioning theory. By using a clonal selection optimizing algorithm, optimal calculation is performed on remote sensing image subpixel positioning without any future knowledge, and a result of subpixel positioning can be directly obtained according to an input image. Meanwhile, as the clonal selection algorithm has the advantages of self-learning and self-memorizing, the global optimal subpixel positioning result can be obtained.

Description

A sub-pixel location method for hyperspectral remote sensing images based on clonal selection technical field The invention belongs to the field of remote sensing image processing, and in particular relates to a new method for locating sub-pixels of hyperspectral remote sensing images based on clone selection. Background technique In remote sensing images, especially hyperspectral remote sensing images, due to the influence of spatial resolution, there is a common phenomenon of mixed pixels, that is, a pixel is composed of multiple ground objects. The mixed pixel decomposition technology can obtain the abundance corresponding to each object category in the pixel, and obtain an abundance image equal to the number of categories, which effectively solves the problem of pixel mixing (Zhang Liangpei and Zhang Lifu, 2005). However, the mixed pixel decomposition can only obtain the abundance of each endmember component, and cannot determine the specific position of various ground...

AI Technical Summary

Problems solved by technology

However, in remote sensing image sub-pixel positioning, CSA has not been well applied, so there is no real sub-pixel positioning method for clonal selection.

Images