Unsupervised hyperspectral image implicit low-rank projection learning feature extraction method

Abstract

The invention discloses an unsupervised hyperspectral image implicit low-rank projection learning feature extraction method, and aims to provide an unsupervised hyperspectral feature extraction methodcapable of realizing rapidness and high robustness. The method is realized through the following technical scheme: firstly, dividing input hyperspectral image data into a training set and a test setin proportion; designing a robustness weight function, calculating the spectral similarity between every two training set samples, and constructing a spectral constraint matrix and a graph regularization constraint according to the training set; approximately decomposing row representation coefficients of the hidden low-rank representation model; constructing an implicit low-rank projection learning model by combining the spectral constraint matrix and the image regularization constraint; and optimizing and solving the hidden low-rank projection learning model by adopting an alternating iterative multiplier method, obtaining a low-dimensional projection matrix, outputting the categories of all test set samples, taking the low-dimensional features of the training set as the training samplesof the support vector machine, classifying the low-dimensional features of the test set, and evaluating the feature extraction performance according to the quality of a classification result.

Description

technical field [0001] The invention relates to remote sensing image processing technology in many fields such as aviation, aerospace, agricultural management, disaster forecast, environmental monitoring, resource exploration, land planning and utilization, disaster dynamic monitoring, crop yield estimation, weather forecast, etc., specifically relates to unsupervised hyperspectral image hidden Low-rank projection learning method for feature extraction. Background technique [0002] Hyperspectral image has the characteristics of map-spectrum integration, and it is a new remote sensing technology developed recently at home and abroad. Compared with multispectral images, hyperspectral images have more spectral bands, high spectral resolution, and narrow band width, which can distinguish and identify ground objects with high reliability. However, these advantages of hyperspectral image are at the cost of its high data dimension and large amount of data, and the correlation bet...

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Problems solved by technology

[0007] The purpose of the present invention is to provide a fast and robust unsupervised hyperspectral feature extraction method for hyperspectral data with high spectral dimensionality, large information redundancy, and few labeled samples to make up for existing features. Insufficient Extraction Techniques

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