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Nearest neighborhood hyper-spectral image classification method based on dictionary and band restructuring

A hyperspectral image and classification method technology, applied in the field of spectral data classification, can solve the problems of low classification accuracy of hyperspectral images, loose space-spectral information integration, high time complexity, etc., to increase classification accuracy and time complexity Low, the effect of improving the classification accuracy

Active Publication Date: 2015-08-05
XIDIAN UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to solve the technical problems that the current single classifier is not high in classification accuracy of hyperspectral images, and the classification effect is not good; while the ordinary space-spectrum combination method has high time complexity and the combination of space-spectrum information is not tight. A low-time complexity, high-precision nearest neighbor hyperspectral image classification method based on dictionary and band recombination, which specifically includes the following steps:

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  • Nearest neighborhood hyper-spectral image classification method based on dictionary and band restructuring
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  • Nearest neighborhood hyper-spectral image classification method based on dictionary and band restructuring

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Embodiment 1

[0041] The present invention is a space-spectrum combination method based on dictionary and band recombination for rapid classification of nearest neighbor hyperspectral images, see figure 1 , including the following steps:

[0042] Step 1 read hyperspectral remote sensing data image, hyperspectral remote sensing data image referred to as hyperspectral image.

[0043] Step 2: Preprocess the read hyperspectral image first, use the spatial neighborhood information of the hyperspectral image, take the L nearest neighbor pixels for each pixel of the hyperspectral image, and perform L-neighborhood mean processing to obtain the preprocessed hyperspectral image. In the hyperspectral initial image, there are a certain number of pixels polluted by noise. Without preprocessing, these pixels are very destructive to the classification accuracy. Therefore, before classification, the present invention utilizes the hyperspectral image Spatial information, noise pixels are processed.

[00...

Embodiment 2

[0050] The nearest neighbor hyperspectral image rapid classification method based on the space-spectrum combination of dictionary and band recombination is the same as embodiment 1, in step 2, each pixel in the hyperspectral image is carried out L-neighborhood mean value processing, used in the present invention The obtained hyperspectral data is the Indian Pines hyperspectral image in the AVIRIS data set, which contains a total of 200 bands, specifically:

[0051] It is assumed that the hyperspectral image has N wave bands in total, and in the experiments of the present invention, N=200. A certain pixel in the hyperspectral image is x i ={x i,1 ,...,x i,N}, with x i As the center, take the L neighboring pixels {x 1 ,...,x L}, in the experiments done by the present invention, L=24. where x 1 ={x 1,1 ,...,x 1,N}∈R N , R N is the N-dimensional feature space, x 2 ={x 2,1 ,...,x 2,N}∈R N , and so on, for x i Do 24-average processing for all L+1=25 pixels in the 5×5...

Embodiment 3

[0055] The nearest neighbor hyperspectral image classification method based on dictionary and band recombination is the same as in embodiment 1-2, in step 3, there are m pixels {x i,1 ,...,x i,m},make

[0056] x i = 1 m Σ j = 1 m x i , j - - - ( 2 )

[0057] Perform the above average processing on all bands to get {x 1 ,...,x N}, N=200, for {x 1 ,...,x N} sort from small to large to obtain the hyperspectral image mean sequence, and then rearrange the hyperspectral bands according to this sequence, and divide the reorganized N bands into n equal parts in order to form n sub-bands. In the present invention, the Indian image is divided into 3 ...

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Abstract

The invention discloses a nearest neighborhood hyper-spectral image classification method based on a dictionary and band restructuring, comprising the following steps: performing L-neighborhood equalization processing on each pixel based on spatial information of a hyper-spectral image; restructuring bands according to the mean value of the pixels of each band and equally dividing the band obtained from restructuring into n sub bands; randomly selecting part of the pixels to constitute a dictionary (the remaining pixels constitute a test sample set), dividing the dictionary and the test sample set into n sub dictionary and sub test sample sets according to sub bands, and making nearest neighborhood decision on the corresponding sub dictionaries to obtain n initial classification maps; and making n-KNN decision to obtain a final result map. The following problems are solved, namely, the classification accuracy is not high, the classification effect is not good, and the time complexity is high and spatial-spectral information combination is not close for an ordinary spatial-spectral combination method. Band restructuring and segmentation is introduced to make multi-band multi-dictionary decision. By adopting a spatial-spectral combination method in classification, a high-precision classification map can be obtained in a short period of time. The method of the invention is high in precision and low in time complexity.

Description

technical field [0001] The invention belongs to the technical field of spectral data classification, and mainly relates to the classification of hyperspectral remote sensing data, in particular to a space-spectrum combination method for rapid classification of nearest neighbor hyperspectral images based on dictionaries and band recombination. It can be used in cartography, marine remote sensing, vegetation survey, atmospheric research, agricultural remote sensing, environmental monitoring and other fields. Background technique [0002] Hyperspectral remote sensing technology has developed rapidly since the 1980s. Hyperspectral records the continuous spectral characteristics of ground objects with its rich band information, and has the possibility of recognizing more types of ground objects and classifying objects with higher accuracy. However, the high spectral dimension and spectral resolution of hyperspectral images bring great opportunities for ground object classificati...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/66
Inventor 慕彩红焦李成云智强熊涛刘红英冯婕田小林张文龙吴生财
Owner XIDIAN UNIV
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