Hyperspectral Remote Sensing Image Classification Method Based on Adaptive Hierarchical Multi-scale

Abstract

The invention discloses a hyperspectral remote sensing image classification method based on self-adaptive layered multi-scale, comprising the following steps: step 1, calculating the irregular neighborhood structure of the pixel according to the spectral angle; step 2, calculating the irregular neighborhood structure in the irregular neighborhood structure Among them, the scale parameters of each layer are determined layer by layer according to the Ka measure, and the corresponding kernel matrix of each layer is calculated layer by layer, and then the weight of the kernel function of each layer is obtained by using the maximum projection variance, so as to obtain an adaptive layered multi-scale kernel function; steps 3. Map the hyperspectral image to the kernel space of the self-adaptive hierarchical multi-scale kernel function obtained in step 2, and use the linear representation of the pixel to be tested based on the dictionary composed of known training sample pixels to obtain a reconstructed sparse matrix, and Assign the pixel under test to the optimal reconstruction category. The invention can quickly and accurately classify hyperspectral remote sensing data.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, in particular to a hyperspectral remote sensing image classification method based on self-adaptive hierarchical multi-scale. Background technique [0002] Hyperspectral image classification is an important application direction of hyperspectral image remote sensing. Hyperspectral image classification determines a category mark for each pixel, which is an analysis method to describe the types of ground objects. The result of the category of ground objects can clearly reflect the spatial distribution of ground objects, which is convenient for people to understand and discover laws. Compared with traditional remote sensing image classification, hyperspectral image classification has the following difficulties: 1) high data dimension, insufficient training samples; 2) many bands, high correlation between bands; 3) obvious intra-class differences; 4) large amount of data , oft...

AI Technical Summary

Problems solved by technology

Although the sparse representation classification method has a good classification effect, it is still difficult to distinguish those linearly inseparable data. In order to overcome this defect, some scholars have adopted the kernel sparse representation algorithm, which inherits the excellent performance of the kernel method. The data is projected into a high-dimensional feature space, so that the data is linearly separable

[0004] However, there are still problems with the above methods: 1) Due to the high correlation of dictionaries in the basic framework of sparse representation, the hyperspectral classification problem based on sparse representation is ill-posed, and there are a large number of local approximate solutions. Prior information introduces regular terms to constrain the range of solutions

However, there is no kernel function that is universally applicable to all classification situations

And a single kernel function limits the possibility of mining more hyperspectral information, especially for multi-category situations

Moreover, a single kernel function has no advantage in maintaining the classification accuracy and the generalization ability of the model.

3) The problem of parameter selection in the kernel method is also a big problem. The traditional method of parameter testing is cross-checking, but while the parameters increase, it is necessary to train the data repeatedly, and the time cost is large.

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