Hyperspectral Image Classification Method Based on Joint Unmixing and Adaptive Endmember Extraction

Abstract

The invention discloses a method for classifying hyperspectral images on the basis of a combination of unmixing and adaptive end member extraction. The method is used for solving the technical problem of large errors of an existing method for classifying hyperspectral images on the basis of spectral unmixing. The technical scheme includes that the method comprises steps of roughly classifying the images, and extracting end member sets of various categories by the aid of a confusion matrix; linearly spectrally unmixing training samples in the various categories by the aid of the acquired end member sets, and acquiring optimal classification results by the aid of a probability classifier with an abundance value optimized on the basis of multinomial logistic regression; updating the end member sets of the various categories according to the classification results; iterating the procedure, and continuously optimizing the classifier so that the classification accuracy is improved. The method has the advantage that as shown by test results, the average accuracy of tests on a simulated data set, the average accuracy of tests on data of a true hyperspectral data set AVIRIS Indian Pine and the average accuracy of tests on data of a true hyperspectral data set ROSIS Pavia University are 81.98%, 62.19% and 82.38% respectively.

Description

technical field [0001] The invention relates to a hyperspectral image classification method, in particular to a hyperspectral image classification method of joint unmixing and self-adaptive endmember extraction. Background technique [0002] Hyperspectral image classification technology is of great significance for the realization of fine surface detection, environmental monitoring, and disaster warning. The existing hyperspectral image classification methods mainly include: hard classification methods based on pure pixel theory and probabilistic soft classification methods for mixed pixels. In hyperspectral images, low spatial resolution will cause pixels to contain multiple types of ground objects, so the object-oriented probabilistic soft classification method is a research hotspot in recent years. It achieves classification by estimating the probability that a mixed pixel belongs to a certain object. [0003] The document "Spectralunmixingfortheclassificationofhyperspe...

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Problems solved by technology

[0004] In order to solve the problem of mixed pixels in hyperspectral images and overcome the shortcomings of existing hyperspectral image classification methods based on spectral unmixing with large errors, the present invention provides a hyperspectral image classification method based on joint unmixing and adaptive endmember extraction

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