Hyperspectral image classification method based on guided filtering and kernel extreme learning machine

Abstract

The invention relates to the technical field of hyperspectral image classification, and discloses a hyperspectral image classification method based on guided filtering and a kernel extreme learning machine, and the method comprises the following steps: S1, carrying out the normalization preprocessing of hyperspectral image data; s2, performing principal component analysis on the normalized hyperspectral image data to obtain a plurality of different principal component images; s3, performing guided filtering processing on the input image through the guided image, and extracting space-spectral information; s4, selecting 5% of the images after guided filtering processing as a training set, and taking the remaining images as a test set; s5, performing linear weighting on the space spectrum kernel and the spectrum kernel in a linear weighting mode to form a weighting kernel; s6, the weighted kernel is sent to a kernel extreme learning machine, then the trained kernel extreme learning machine is used for carrying out classification prediction on the test sample, and the hyperspectral image classification method has the advantages that the classification precision is very high, and the time cost spent in the whole classification process is very low.

Description

technical field [0001] The invention relates to the technical field of hyperspectral image classification, in particular to a hyperspectral image classification method based on guided filtering and kernel extreme learning machine. Background technique [0002] Hyperspectral remote sensing is based on spectroscopy. Its spectral band width is less than 10nm. It can acquire many very narrow and spectrally continuous image data in the ultraviolet, visible, near-infrared and short-wave infrared regions of the electromagnetic spectrum. Each pixel in the image corresponds to There are as many as hundreds of bands, and the contained spectral information is very rich. Because hyperspectral images have both optical properties and spectral recognition capabilities, they have gradually become the frontier and hotspot of research and application in the field of remote sensing. So far, hyperspectral remote sensing has been successfully applied to fine terrain classification, mineral mapp...

AI Technical Summary

Problems solved by technology

However, due to the high dimensionality of hyperspectral images, as the dimensionality increases, the classification accuracy does not increase but decreases, resulting in the disaster of dimensionality

At the same time, some categories have fewer labeled samples, and it takes a lot of time to achieve high-precision classification.

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