Active Spectral Clustering Method for Remote Sensing Images

Abstract

The invention provides a method for active spectral clustering of remote sensing images. Firstly, characteristics of the remote sensing images are extracted, and a k-NN graph is established and used for carrying out spectral clustering on the remote sensing images; then, the remote sensing image with the most chaotic label in neighborhoods is selected out actively, questioning is carried out on sides of the remote sensing image with the most chaotic label with the sides of the neighborhoods to obtain paired constraint information, the k-NN graph is purified by using the paired constraint information, and the spectral clustering is carried out on the remote sensing images again based on the purified k-NN graph. By means of the method for the active spectral clustering of the remote sensing images, data marker difficulty is lowered, and requirements for the professional knowledge of operators are not high; the accuracy of clustering results of the remote sensing images and analysis processing capability on the massive remote sensing images are improved significantly.

Description

technical field [0001] The invention belongs to the field of remote sensing image analysis, in particular to an active spectrum clustering method for remote sensing images. Background technique [0002] With the development of remote sensing technology, the resolution and quantity of remote sensing images have increased rapidly, and a large amount of remote sensing image data with high spatial resolution and high spectral resolution has been accumulated. However, on the one hand, the image interpretation workload of these massive remote sensing image data is heavy; on the other hand, due to the increase in image resolution, automatic intelligent interpretation is difficult. At present, these massive remote sensing image data have not been effectively utilized. [1] . How to extract useful scene information from a large amount of remote sensing image data is one of the important tasks and technical problems of remote sensing image analysis. [0003] Although the supervised ...

AI Technical Summary

Problems solved by technology

Most current active clustering algorithms limit the number of clusters to two categories [3～4] , The complexity of the algorithm implementation is too high [5] , Active learning objects are only aimed at boundary points and sparse points [4]

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