Research on Classification of Remote Sensing Image Based on SVM Including Textural Features

Feng Hua Huang

doi:10.4028/www.scientific.net/AMM.543-547.2559

Paper Titles

A New Medical Image Enhancement Method Based on Human Visual Characteristics
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Thinning Algorithm of Binary Image Based on Improved Templates
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Research on Frequency Hopping Parameters of Hybrid Spread Spectrum Signal
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Research on Soft Demodulation of Telemetry Signal in Reentry Telemetry Environment
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Research on Classification of Remote Sensing Image Based on SVM Including Textural Features
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Realization on eNodeB of PCFICH in LTE System
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A Blind Extraction Method of FH Signals Based on Improved Symmetric Co-Occurrence Matrix in HF Channel
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Capacity Analysis for Multiple Antennas Network in Spectrum Sharing System
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MIMO Channel Research and Hardware Implementation
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 543-547Research on Classification of Remote Sensing Image...

Research on Classification of Remote Sensing Image Based on SVM Including Textural Features

Abstract:

In order to solve the problems in the traditional remote sensing image based on spectral information, such as low classification accuracy, different object with the same spectral features or the same object with the different spectral features, and limited sample quantity and so on, a remote sensing image classification method based on the support vector machine (SVM) including with textural features is proposed. Using Langqi Island of Fuzhou as experimental area, preprocessing and principal component analysis were made to initialize TM images, and the spectral features and GLCM-based textural features of ground objects were extracted and analyzed respectively. Then, the extraction, training and testing of samples based on the two types of features were finished for training various SVM classifiers, which were used for classifying land use in the experimental area. Through the maximum likelihood method, the BP neural network and the support vector machine (SVM), a crossed classification and contrast experiment was made to two different types of samples based on the simple spectral features and the features combined with texture respectively. The experimental results showed that the SVM classification method including textural features can effectively improve the accuracy of land use classification, and therefore it can be promoted better.