Object-based OBIA-SVM-CNN remote sensing image classification method

A technology of OBIA-SVM-CNN and RBF-SVM, which is applied in the direction of instruments, character and pattern recognition, and computer parts, can solve the problems of low accuracy of remote sensing classification and recognition, and improve the accuracy of remote sensing classification, improve classification accuracy, The effect of overall classification accuracy improvement
CN109740631AActive Publication Date: 2019-05-10NORTHEAST INST OF GEOGRAPHY & AGRIECOLOGY C A S
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
NORTHEAST INST OF GEOGRAPHY & AGRIECOLOGY C A S
Publication Date
2019-05-10

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Abstract

The invention provides an object-based OBIA-SVM-CNN remote sensing image classification method, and relates to a remote sensing image classification method. The objective of the invention is to solvethe problem of low remote sensing classification and identification accuracy of an existing complex farmland area. The method comprises the following steps: 1, segmenting a remote sensing image basedon a multi-scale segmentation algorithm until all segmented objects are matched with farmland plaque boundaries on the remote sensing image through visual inspection; 2, trainning RBF-SVM model and CNN model to get trained RBF-SVM model and CNN model; 3, using the trained RBF-SVM model and the CNN model to classify and predict each segmented object in the first, and obtaining the prediction results of the CNN model and the prediction results of the SVM model.; and 4, performing decision fusion on the CNN model prediction result and the SVM model prediction result to obtain a final classification result. The method is applied to the field of remote sensing image classification.
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Description

technical field

[0001] The invention relates to a remote sensing image classification method. Background technique

[0002] The real-time monitoring of the spatio-temporal distribution information of farmland is of great significance for the estimation of farmland yield and the protection of food security on a national scale and even on a global scale. Remote sensing technology has developed into one of the mainstream means of farmland monitoring and classification because it has many unique advantages, including macroscopicity, current situation, repeatability and economy. With the rapid development of modern remote sensing technology, users can now obtain a large number of high-resolution (high spatial resolution, HSR) remote sensing images, which provides new opportunities for many remote sensing applications (including farmland monitoring and classification). However, although high-resolution images have richer structural information and texture information, compared wi...

Claims

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