High spectral image classification method based on morphological characteristics and dictionary learning

A hyperspectral image and morphological feature technology, applied in the field of hyperspectral image classification, can solve the problems of few samples and high dimensionality
CN106203510AInactive Publication Date: 2016-12-07NANJING UNIV
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
NANJING UNIV
Publication Date
2016-12-07
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses a high spectral image classification method based on morphological characteristics and dictionary learning. The method comprises the following steps of extracting morphological characteristics from high spectral images, a process of dictionary learning, coding the characteristics, and classifying the images. The method is applied to the field of high spectral image classification, the structural relation of space information in the high spectral images is taken into full consideration, high-level semantic mapping is constructed on the basis of the space relation information, high-level semantic codes which can maintain the structure information of the characteristic space effectively are obtained and used for a high spectral image classification task, and the problem that semantic gaps exist between the high level meanings and bottom characteristics of the high spectral images is overcome. The method has substantial effect in high spectral image classification, and has higher application values.
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Description

technical field

[0001] The invention belongs to the field of image classification, in particular to a hyperspectral image classification method based on morphological features and dictionary learning. Background technique

[0002] With the development of remote sensing technology and computer technology, hyperspectral remote sensing images have penetrated into various fields of society and economy. At the same time, the number of hyperspectral images is also increasing day by day. How to organize images and classify hyperspectral images has become an important research topic in the field of remote sensing information technology. Due to the characteristics of high dimensionality and few samples of hyperspectral images, traditional hyperspectral image classification methods only consider the spectral characteristics and ignore the spatial characteristics of images, and there is a natural "interference" between digital storage of images and human semantic understanding. Semant...

Claims

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