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An Unsupervised Clustering Method for Classification of Large-scale Spectral Remote Sensing Images

A technology of remote sensing image and clustering method, which is applied in the field of spectral remote sensing image ground object classification, to achieve the effects of accelerating classification speed, high computing efficiency, and reducing computing redundancy

Active Publication Date: 2019-11-29
BEIHANG UNIV
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Problems solved by technology

[0028] The technical problem to be solved by the present invention is: in view of the respective shortcomings of the above three classification schemes, in order to solve the problem of classification of ground objects in large data volume remote sensing images, the present invention proposes an unsupervised clustering method for large data volume spectral remote sensing image classification

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  • An Unsupervised Clustering Method for Classification of Large-scale Spectral Remote Sensing Images
  • An Unsupervised Clustering Method for Classification of Large-scale Spectral Remote Sensing Images
  • An Unsupervised Clustering Method for Classification of Large-scale Spectral Remote Sensing Images

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[0045] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0046] Technical scheme block diagram of the present invention is as figure 2 As shown, the basic technical principles are as follows.

[0047] Step 1: Spectrum selection.

[0048] Hyperspectral remote sensing images contain hundreds of continuous spectral segments and have a large amount of data. In the process of image processing, the spectral segment selection method is often used to select the optimal feature spectral segment, sacrificing some classification accuracy to greatly improve the efficiency of classification processing. The complexity of various spectral band selection methods is different. Here is a brief introduction to the principal component analysis method in [1].

[0049] As shown in formula (1), assuming that the original image data Y contains N pixels and L spectral segments, the correlation matrix defining the data is...

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Abstract

The invention discloses a non-supervised clustering method for classification of spectral remote sensing images with a large amount of data. The original data is divided into several data blocks, and the cluster centers of each data sub-block are obtained by the peak density search method; the cluster centers are re-divided into several data blocks, and clustered again by the peak density search method to reduce the number of cluster centers; repeat In the block-clustering process, a two-dimensional matrix can be used to represent the similarity between any two cluster centers, and then the final classification result can be obtained. The advantage of the method of the present invention is: good applicability, not only can be used for classification of hyperspectral remote sensing images with more spectral segments, but also suitable for classification of multi-spectral remote sensing images with fewer spectral segments or hyperspectral remote sensing images after spectral segment selection; The operation efficiency is high, and the block processing reduces the calculation redundancy of the similarity matrix, and because the clustering processing of each data block is independent of each other, parallel processing can be used to speed up the classification rate.

Description

technical field [0001] The invention relates to the technical field of spectral remote sensing image ground object classification, in particular to a non-supervised clustering method for large data volume spectral remote sensing image classification. Background technique [0002] Hyperspectral and multispectral remote sensing images record the radiation characteristics of the same area in different observation spectrum bands. Due to the significant differences in the spectral radiation characteristics of various surface objects such as vegetation, soil, buildings, and water bodies, the spatial distribution information and spectral radiation characteristic information of different types of surface objects can be obtained by analyzing spectral remote sensing data. These classification results have important applications in the fields of surface vegetation distribution research, soil and geological exploration, urban cover survey, and water quality monitoring. [0003] Unsuper...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
CPCG06F18/23213G06F18/24137
Inventor 何晓雨许小剑
Owner BEIHANG UNIV
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