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Remote-sensing image semi-supervised projection dimension reducing method based on local consistency

A remote sensing image and consistency technology, applied in the field of image processing, can solve the problems of incompatibility between band correlation and data information volume, affect classification recognition rate, and large correlation between bands, so as to maintain the consistency of similar objects, The effect of improving classification recognition rate and high recognition rate

Inactive Publication Date: 2013-01-30
XIDIAN UNIV
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Problems solved by technology

The disadvantage of this method is that the method of this patent application cannot have both the band correlation and the amount of data information. Under the condition of the maximum amount of information, the correlation between the bands will be large, which will affect the classification recognition rate.

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  • Remote-sensing image semi-supervised projection dimension reducing method based on local consistency
  • Remote-sensing image semi-supervised projection dimension reducing method based on local consistency
  • Remote-sensing image semi-supervised projection dimension reducing method based on local consistency

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Embodiment Construction

[0027] refer to figure 1 , the present invention is described in further detail.

[0028] Step 1, divide the remote sensing image dataset.

[0029] Take the hyperspectral data set to be processed as the test set D ∈ R d×N , according to the training-test sample ratio, select labeled samples to form a supervised training set A∈R d×M ; Among them, d represents the sample feature dimension, N represents the total number of all samples in the test set, and M represents the total number of all samples in the training set. In the embodiment of the present invention, the sample feature dimension d is 200, the total number N of all samples in the test set is 6929, and the total number M of all samples in the training set is 689, 228, 113, 74 and 55 in sequence.

[0030] Step 2, generate matrix.

[0031] 2a) Using the semantic similarity matrix formula to generate the label matrix of the test set, the semantic similarity matrix formula is as follows:

[0032]

[0033] Among the...

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Abstract

The invention discloses a remote-sensing image semi-supervised projection dimension reducing method based on local consistency. The method includes the following steps: (1) dividing a remote-sensing image data set; (2) generating a semantics similar matrix, a neighbor matrix and a location consistency matrix; (3) mixing a label matrix and the neighbor matrix; (4) generating neighbor mean vector; (5) generating an alien divergence matrix, a similar divergence matrix and a local consistency divergence matrix; (6) calculating an optimum projection matrix; and (7) conducting projection and dimension reducing. The method adopts the semi-supervised learning based on local consistency binding and improves recognition rate under small sample learning condition.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a remote sensing image semi-supervised projection dimensionality reduction method based on local consistency in the technical field of information extraction and pattern recognition. The present invention can be used to classify ground objects in the technical field of hyperspectral remote sensing images, reduce redundant bands through the band dimension reduction method, improve the classification accuracy of remote sensing images, and use hyperspectral images to analyze complex landforms and land objects to determine different categories features. Background technique [0002] At present, in the field of hyperspectral remote sensing images, methods for dimensionality reduction of hyperspectral data are usually divided into two categories: feature extraction and feature selection methods. The feature extraction method uses the original data to extract its charact...

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Application Information

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IPC IPC(8): G06K9/62
Inventor 杨淑媛焦李成徐雯晖刘芳缑水平侯彪王爽杨丽霞邓晓政王秀秀
Owner XIDIAN UNIV
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