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Unsupervised clustering method used for large data volume spectral remote sensing image classification

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: 2018-10-12
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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  • Unsupervised clustering method used for large data volume spectral remote sensing image classification
  • Unsupervised clustering method used for large data volume spectral remote sensing image classification
  • Unsupervised clustering method used for large data volume spectral remote sensing image classification

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Embodiment Construction

[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 an unsupervised clustering method used for large data volume spectral remote sensing image classification, comprising the following steps: dividing the original data into a plurality of data blocks, and obtaining a cluster center of each data subblock by virtue of a peak density searching method; dividing each cluster center into a plurality of data blocks again, and clustering again by virtue of the peak density searching method, so that number of the cluster centers is reduced; and repeating a partitioning-clustering process until similarity of any two cluster centerscan be represented by using one two-dimensional matrix, and then obtaining a final classification result. The unsupervised clustering method disclosed by the invention has the advantages that applicability is good, so that the method not only can be used for hyperspectral remote sensing image classification with more spectrum bands but also can be used for hyperspectral remote sensing image classification with fewer spectrum bands after multispectral remote sensing image or spectrum band selection; and operation efficiency is relatively high, blocked processing reduces computation redundancyof a similarity matrix, and clustering processing of all the data blocks is mutually independent, so that parallel processing can be adopted, and classification rate is increased.

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 Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/23213G06F18/24137
Inventor 何晓雨许小剑
Owner BEIHANG UNIV
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