Remote sensing image classification method based on Gabor features and EMAP features

A technology of remote sensing images and classification methods, applied in the field of remote sensing image interpretation, recognition and processing, can solve the problems of small number of samples, unbalanced categories, redundancy, etc., and achieve the effect of avoiding adverse effects and improving the classification effect.

Pending Publication Date: 2021-12-14
BEIHANG UNIV
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Problems solved by technology

[0006] To this end, the present invention proposes a fusion feature expression method, which combines spectral information and spatial information by fusing Gabor features and EMAP features to avoid the adverse effects of original spectral data, ove

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  • Remote sensing image classification method based on Gabor features and EMAP features
  • Remote sensing image classification method based on Gabor features and EMAP features
  • Remote sensing image classification method based on Gabor features and EMAP features

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Embodiment Construction

[0069] The present invention will be further described below in conjunction with the accompanying drawings and embodiments. It should be understood that the following embodiments are intended to facilitate the understanding of the present invention, and have no limiting effect on it.

[0070] In the classification method provided by the present invention, in order to reduce the data dimension and shorten the calculation time, firstly, the hyperspectral image is processed through principal component analysis to obtain the first three principal components of the hyperspectral image. As an unsupervised spectral feature extraction algorithm, principal component analysis can extract spectral features from hyperspectral images, and also plays a role in feature dimensionality reduction. The first three principal components can retain most of the effective information and reduce the impact of noise.

[0071] It is defined that the size of the hyperspectral image is M N D, so the size ...

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Abstract

The invention discloses a remote sensing image classification method based on Gabor features and EMAP features. The method comprises the steps: extracting Gabor features from a remote sensing image, constructing a Gabor feature kernel, extracting EMAP features from the remote sensing image, and constructing an EMAP feature kernel; fusing the constructed Gabor feature kernel and EMAP feature kernel by using a feature composite kernel framework to obtain a feature composite kernel; and on the basis of the obtained feature composite kernel, classifying samples in the remote sensing image through a multi-term logistic regression model. According to the method, the Gabor features and the EMAP features are combined with spectral information and spatial information, so that the adverse effect of original spectral data is avoided, the problems of unbalanced categories and few samples are solved, the problem of information redundancy caused by relatively strong correlation of the spectral information is effectively solved, and the classification precision is improved.

Description

technical field [0001] The invention belongs to the field of interpretation, recognition and processing of remote sensing images, in particular to a classification method of hyperspectral remote sensing images based on Gabor features and extended multi-attribute profile (EMAP) features. Background technique [0002] In hyperspectral remote sensing images, the spectral information of different types of ground objects is different. Hyperspectral image classification technology is of great significance to promote the development of remote sensing technology applications, but this technology faces problems such as high dimensionality of hyperspectral image data, few labeled samples, mixed pixels, and low data quality. [0003] Aiming at the problem of few labeled samples of hyperspectral images, scholars at home and abroad have proposed a series of hyperspectral image classification algorithms. At present, the most widely used supervised learning algorithm. The supervised lear...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2415G06F18/253
Inventor 江玲周付根史洁玉
Owner BEIHANG UNIV
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