Super-pixel-based prototype spectrum set generation method

Abstract

The invention discloses a prototype spectrum set generation method based on superpixels, and belongs to the field of hyperspectral image processing, and the method specifically comprises the steps: firstly, collecting spectrum wave bands of a certain region, taking each wave band as an image, and forming an image set X; then, 40% of wave bands are randomly selected, pixel points in the image corresponding to the wave bands are averaged, and an image Y is formed; thirdly, SSIM structure indexes of the images in the image set X and the image Y are calculated one by one to serve as scores of the images, and the scores are arranged in a descending order; re-selecting the first 40% wave band to regenerate the image Y, and repeatedly calculating the score of each image in the image set X until the wave band is stable; and finally, selecting the first three wavebands with the highest score, inputting the first three wavebands into a superpixel segmentation algorithm to extract superpixel small blocks at fixed intervals, taking the average spectrum of each superpixel small block as an initial clustering center of a k-means clustering algorithm, and performing clustering to obtain a prototype spectrum set of the region. According to the method, the calculation amount of the clustering process is reduced, and the clustering result is stabilized.

Description

technical field [0001] The invention belongs to the field of hyperspectral image processing, in particular to a method for generating prototype spectrum sets based on superpixels. Background technique [0002] In recent years, with the wide application of hyperspectral sensors, hyperspectral image processing technology has also developed, and has played an increasingly important role in military, agricultural and forestry, environmental protection and industrial fields. [0003] In hyperspectral remote sensing technology, the standard spectrum of ground objects is an important basis for hyperspectral remote sensing technology, and the spectral curve is the main basis for band selection and target classification. However, due to various factors such as hyperspectral being greatly affected by light conditions, less labeled hyperspectral images, and slight differences in the standard spectrum of ground objects in different environments, it is difficult to extract the standard s...

AI Technical Summary

Problems solved by technology

[0005] Shijin Li and Jianbian Qiu proposed a method to generate prototype spectra in 2013, which generates corresponding prototype spectrum sets by clustering hyperspectral images; Unstable, easy to fall into a local optimal situation, unable to obtain an effective prototype spectrum set

[0006] The common region segmentation algorithm that can construct superpixels is mainly the k-means clustering method, which directly performs k-means clustering on hyperspectral images, and uses the stable cluster centers obtained by clustering as the prototype spectral set of hyperspectral images; However, doing so will lead to high computational time complexity; the cluster centers generated by k-means clustering are unstable, and the results of each experiment are inconsistent, and the robustness is poor; there is no clear criterion for determining the number of cluster centers. Multiple experiments are required for best results

Images