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Classification and construction method for semi-supervised support vector machine (SVM) remote sensing image

A technology of remote sensing images and construction methods, applied in the fields of instruments, character and pattern recognition, calculation models, etc., can solve the problems of insufficient classification accuracy, low classification, and difficulty in finding the optimal classification parameters, so as to overcome the low classification accuracy and avoid searching for inaccurate effect

Inactive Publication Date: 2013-01-16
NORTHEAST INST OF GEOGRAPHY & AGRIECOLOGY C A S
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Problems solved by technology

[0003] The present invention aims at the problem that it is not easy to find the optimal classification parameters when the existing SVM remote sensing image technology is applied, and the classification accuracy is low due to the hardening points of mixed pixels and insufficient training samples, and proposes a new semi-supervised SVM remote sensing image classification method

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  • Classification and construction method for semi-supervised support vector machine (SVM) remote sensing image
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  • Classification and construction method for semi-supervised support vector machine (SVM) remote sensing image

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specific Embodiment approach 1

[0044] Specific implementation mode one: combine figure 1 Describe this embodiment, the specific steps of a kind of semi-supervised SVM remote sensing image classification construction method described in this embodiment are as follows:

[0045] Step 1. Optimizing the SVM remote sensing image parameters using the adaptive mutation particle swarm optimization algorithm, that is, the PSVM algorithm. The specific steps of the PSVM algorithm are as follows:

[0046] Step 1 (1), randomly initialize the position and velocity of the particles in the particle swarm;

[0047] Step 1 (2), the p of the particle b set to the current position, p g Set to the best particle position in the initial population;

[0048] Step 1 (3), judging whether the algorithm meets the convergence conditions, if so, execute step 1 (8), otherwise execute step 1 (4); the global extremum found by the entire particle swarm at the final convergence position of the particle swarm optimization algorithm, using g...

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Abstract

The invention relates to a classification and construction method, in particular to a classification and construction method for a semi-supervised support vector machine (SVM) remote sensing image. The problems of incapability of easily finding an optimal classification parameter and low classification accuracy caused by the hard classification of mixed pixels and insufficiency of training samples during the application of the conventional SVM remote sensing image technology are solved. The method specifically comprises the following steps of: 1, optimizing an SVM remote sensing image parameter by utilizing an adaptive mutation particle swarm algorithm; and 2, constructing a PS3VM semi-supervised classification model by utilizing a self-training method. The method is used for constructing the semi-supervised SVM remote sensing image.

Description

technical field [0001] The invention relates to a classification construction method, in particular to a semi-supervised SVM remote sensing image classification construction method. Background technique [0002] Remote sensing images contain rich and complex ground object information, which contain many types of data and high ambiguity. How to effectively improve the speed and accuracy of image classification is a key issue in remote sensing image research, and it is also the focus of attention. Support vector machine technology (support vector machines, SVM) has achieved good results in remote sensing information acquisition due to its ability to better solve high-dimensional features, nonlinearity, over-learning, and local minima. Certain deficiencies are mainly manifested in: first, there is no particularly good way to select the classification parameters, and it is not easy to find the optimal classification parameters during application; second, the hardening points of...

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Application Information

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IPC IPC(8): G06K9/62G06N3/00
Inventor 刘颖张柏王丽敏顾振山郭勤
Owner NORTHEAST INST OF GEOGRAPHY & AGRIECOLOGY C A S
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