Support Vector Machine and various methods of Multi-Spectral Satellite Image Classification

被引：0
|
作者：
Patki, Priti Sudhir
Kelkar, Vishakha V.
机构：
来源：
2013 INTERNATIONAL CONFERENCE ON ADVANCES IN TECHNOLOGY AND ENGINEERING (ICATE) | 2013年
关键词：
Image Classification; Artificial Neural Network; Fuzzy Measure; Genetic Algorithm; Support Vector Machine;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Use of satellite images is one of the prominent methods for information about land coverage. Multi-Spectral satellite image is an appropriate source for providing this information. Classification of these Multi-Spectral images is an effective way to recover the information. This can be achieved based on the kinds of pattern models used, the types of information used, the manner in which they are applied to the image and the manner in which they partition the image into classes. Here, along with Support Vector Machine (SVM) algorithm, various other classification techniques are discussed and compared based on several parameters.
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页数：5
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