Hyperspectral image classification method based on K nearest neighbor filtering

Abstract

The invention discloses a hyperspectral image classification method based on K nearest neighbor filtering. The classification process mainly includes (1) support vector machine (SVM) classification: rough classification of a hyperspectral image using a SVM classifier to obtain an initial probability graph; (2) principal component analysis dimensionality reduction: dimensionality reduction of the hyperspectral image by way of principal component analysis to obtain a first principal component image; (3) K nearest neighbor filtering: extraction of spatial information of the hyperspectral image under the guidance of the first principal component image based on a non local K nearest neighbor filter to optimize the initial probability graph; and (4) accurate classification of the hyperspectral image according to the optimized probability graph. The greatest advantage of the method in the invention over a traditional hyperspectral classification algorithm is that the non local spatial information of the hyperspectral image can be extracted for optimized classification without solving for a complex global energy optimization problem. Thus, the classification speed is high, and the accuracy is high.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a hyperspectral classification method based on K-nearest neighbor filtering. Background technique [0002] In the 1960s, remote sensing imaging technology developed rapidly, and the spectral resolution continued to improve, from black and white imaging, color photography, to multispectral scanning imaging, and then in 1980, hyperspectral remote sensing imaging technology was born. Hyperspectral remote sensing uses a narrow and continuous spectral channel (generally less than 10nm in band width) to continuously image ground objects. The main difference between it and conventional remote sensing is that the hyperspectral imaging spectrometer can provide dozens of Spectral information of up to hundreds of narrow bands, each pixel can generate a complete and continuous spectral curve. [0003] A hyperspectral image is essentially a three-dimensional data matrix....

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Problems solved by technology

[0005] The purpose of the present invention is to overcome the deficiencies of the prior art, that is, the traditional hyperspectral image spatial spectrum classification method cannot make good use of the global spatial information between the entire hyperspectral image pixels and the hyperspectral image classification based on segmentation is too dependent on In view of the shortcomings of the segmentation algorithm and the slow classification speed, a hyperspectral classification method based on K-nearest neighbor filtering is extracted, that is, the global spatial information of the hyperspectral image is extracted through a filter based on k-nearest neighbor, and combined with support vector machine Combine the obtained hyperspectral classification results to achieve fast and high-precision classification of hyperspectral images

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