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Local and non-local multi-feature semantics-based hyperspectral image classification method

A technology of hyperspectral images and classification methods, applied in the field of hyperspectral image classification based on local and non-local multi-feature semantics, can solve the problems of poor robustness, low classification result accuracy, weak spatial consistency of classification result graphs, etc. Robustness, avoid falling into local optimal solution, improve the effect of spatial consistency

Active Publication Date: 2017-03-22
XIDIAN UNIV
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Problems solved by technology

[0008] Aiming at the problems of low accuracy of classification results, poor robustness, and weak spatial consistency of classification result graphs existing in the prior art, the present invention proposes a method that reasonably combines various feature information and can effectively utilize local Local and non-local multi-feature semantic hyperspectral image classification based on local and non-local spatial information and related category information

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  • Local and non-local multi-feature semantics-based hyperspectral image classification method
  • Local and non-local multi-feature semantics-based hyperspectral image classification method
  • Local and non-local multi-feature semantics-based hyperspectral image classification method

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Embodiment 1

[0028] For the classification of ground objects in hyperspectral images, most of the current existing methods have the problems of unsatisfactory classification accuracy, poor robustness of classification results, and weak spatial consistency of classification result maps. The present invention combines various features in The fusion technology of semantic space and the local and non-local space constraint method mainly propose a semantic hyperspectral image classification method based on local and non-local multi-features to solve various problems existing in the existing methods.

[0029] The present invention is a semantic hyperspectral image classification method based on local and non-local multi-features, see figure 1 , including the following steps:

[0030] (1) Input an image and extract various features of the image.

[0031] Commonly used hyperspectral image data include the Indian Pine dataset and the Salinas dataset obtained by the airborne visible / infrared imagin...

Embodiment 2

[0046] Based on local and non-local multi-feature semantic hyperspectral image classification method with embodiment 1, wherein the multiple features described in step (1), including but not limited to: original spectral features, Gabor texture features, differential morphology features (DMP ), where Gabor texture features and differential morphology features (DMP) are expressed as follows:

[0047] Gabor texture features: for hyperspectral images Perform principal component analysis (PCA) processing, take the processed first 3-dimensional principal components as 3 reference images, and perform Gabor transformation in 16 directions and 5 scales respectively, and obtain 80-dimensional texture features for each reference image, and stack Gabor texture features with a total dimension of 240 dimensions are obtained together.

[0048] Differential Morphological Features (DMP): For hyperspectral images Perform principal component analysis (PCA) processing, take the processed fir...

Embodiment 3

[0052] Based on local and non-local multi-feature semantic hyperspectral image classification method with embodiment 1-2, wherein the local and non-local neighbor set construction method described in step (4) is as follows:

[0053] 4a) For the hyperspectral image Perform principal component analysis (PCA), extract the first principal component as a reference image, that is, a grayscale image that can reflect the basic contour information of the hyperspectral image, set the number of superpixels LP, and perform entropy-based The superpixel image segmentation of , get LP superpixel blocks

[0054] In this embodiment, the entropy-based superpixel segmentation method is used to perform superpixel segmentation on the first principal component grayscale image of the hyperspectral image, and the segmented superpixel blocks can well maintain the edge information and structural information in the image, and the segmentation The size difference of the superpixel block is small, and...

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Abstract

The invention discloses a local and non-local multi-feature semantics-based hyperspectral image classification method. The method mainly solves the problem in the prior art that the hyperspectral image classification is low in correct rate, poor in robustness and weak in spatial uniformity. The method comprises the steps of inputting images, extracting a plurality of features out of the images, dividing a data set into a training set and a testing set, mapping various features of all samples into corresponding semantic representations by a probabilistic support vector machine, constructing a local and non-local neighbor set, constructing a noise-reducing Markov random field model, conducting the semantic integration and the noise-reducing treatment, subjecting the semantic representations to iterative optimization, obtaining the categories of all samples based on semantic representations, and completing the accurate classification of hyperspectral images. According to the technical scheme of the invention, the multi-feature fusion is conducted, and the spatial information of images is fully excavated and utilized. In the case of small samples, the advantages of high classification accuracy, good robustness and excellent spatial consistency are realized. The method can be applied to the fields of military detection, map plotting, vegetation investigation, mineral detection and the like.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to machine learning and hyperspectral image processing, and specifically relates to a classification method for hyperspectral image based on local and non-local multi-feature semantics, which is used to classify and recognize different ground objects in the hyperspectral image. Background technique [0002] Hyperspectral remote sensing technology has gradually become a research hotspot in the field of earth observation in the past few decades. Hyperspectral remote sensing technology uses imaging spectrometers to image surface objects simultaneously with tens or hundreds of bands with nanoscale spectral resolution, and can obtain continuous spectral information of surface objects, and realize spatial information, radiation information, and spectral information of surface objects. Acquired synchronously, with the feature of "integration of graphs and graphs". Since different groun...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V20/194G06V20/13G06V10/443G06F18/2155G06F18/2411G06F18/254
Inventor 张向荣焦李成高泽宇冯婕白静侯彪马文萍李阳阳
Owner XIDIAN UNIV
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