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Hyper-spectral image classification method based on non-local similarity and sparse coding
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A non-local similarity, hyperspectral image technology, applied in the field of image processing, can solve problems such as the inability to obtain neighborhood information and the poor classification effect of homogeneous regions.
Active Publication Date: 2014-12-24
XIDIAN UNIV
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Although this method can quickly classify hyperspectral images, it still has the disadvantage that the neighborhood information of samples cannot be obtained well by comparing the Euclidean distance to obtain the neighborhood sample matrix, resulting in poor classification effect in homogeneous regions. it is good
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Embodiment 1
[0065] like figure 1 As shown, the hyperspectral image classification method based on non-local similarity and sparse coding includes the following steps:
[0066] (1) Input hyperspectral image:
[0067] Input the hyperspectral image to be classified, which contains n pixels in total, set each pixel in the input hyperspectral image as a sample, and obtain the sample X of the hyperspectral image=[x 1 ,x 2 ,...,x p ,...,x n ]∈R d ,1≤p≤n, where d is the band number of the hyperspectral image, x p represents the p-th sample of a hyperspectral image, R d Represents a d-dimensional real number vector space;
[0068] (2) Non-local mean filtering:
[0069] The first step is to select a test sample x i , with x i As the center, set a 7×7 neighborhood window;
[0070] The second step is to set a filter window with a size of 3×3, and perform mean filtering on the samples in the neighborhood window;
[0071] The third step is to calculate the test sample x according to the fol...
Embodiment 2
[0110] In this embodiment, on the basis of Embodiment 1, the effects of the present invention are further described in combination with simulation diagrams.
[0112] The hardware test platform of this experiment is: the processor is Intel Core2 CPU, the main frequency is 2.33GHz, the memory is 2GB, and the software platform is: Windows XP operating system and Matlab R2012a. The input image of the present invention is a hyperspectral image Indian Pines, the image size is 145×145×220, the image contains 220 bands and 16 types of ground objects, and the image format is TIF.
[0116] The hyperspectral image classification method propo...
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Abstract
The invention belongs to the technical field image processing and particular relates to a hyper-spectral image classification method based on non-local similarity and sparse coding. The hyper-spectral image classification method based on the non-local similarity and the sparse coding comprises the achieving steps of 1 inputting a hyper-spectral image; 2 filtering a non-local average; 3 determining a training sample set C and a test sample set C'; 4 performing dictionary learning; 5 calculating the sparse coefficient of the test sample set; 6 performing hyper-spectral image classification; 7 outputting classified images. By means of the non-local average filtering method, the defect that in the prior art, only spectral information of the hyper-spectral image is utilized to perform hyper-spectral image classification and accordingly edge portion misclassification is caused is overcome, and the hyper-spectral image classification method can have the advantage that the edge portion misclassification is accurate. In addition, the shortcoming that neighborhood information of the hyper-spectral image cannot be effectively utilized in the prior art is overcome, and the hyper-spectral image classification method can have the advantage that the homogeneous area classification effect is good.
Description
technical field [0001] The invention belongs to the technical field of image processing, in particular to a hyperspectral image classification method based on non-local similarity and sparse coding in the technical field of hyperspectral image classification. The invention can be used to classify the hyperspectral images. Background technique [0002] The improvement of spatial domain and spectral domain resolution of hyperspectral images provides more abundant information for classification, but also brings great challenges. Traditional classification methods, including maximum likelihood classification, decision tree classification, artificial neural network classification, and support vector machine classification, all only classify features from the spectral domain level. However, hyperspectral remote sensing data not only contains rich spectral information of surface objects, but also has specific description and expression of surface object characteristics in two diff...
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