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International Journal of Mining and Geo-Engineering، جلد ۴۹، شماره ۲، صفحات ۲۵۳-۲۶۸
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عنوان انگلیسی High performance of the support vector machine in classifying hyperspectral data using a limited dataset
چکیده انگلیسی مقاله To prospect mineral deposits at regional scale, recognition and classification of hydrothermal alteration zones using remote sensing data is a popular strategy. Due to the large number of spectral bands, classification of the hyperspectral data may be negatively affected by the Hughes phenomenon. A practical way to handle the Hughes problem is preparing a lot of training samples until the size of the training set is adequate and comparable with the number of the spectral bands. In order to gather adequate ground truth instances as training samples, a time-consuming and costly ground survey operation is needed. In this situation that preparing enough field samples is not an easy task, using an appropriate classifier which can properly work with a limited training dataset is highly desirable. Among the supervised classification methods, the Support Vector Machine is known as a promising classifier that can produce acceptable results even with limited training data. Here, this capability is evaluated when the SVM is used to classify the alteration zones of Darrehzar district. For this purpose, only 12 sampled instances from the study area are utilized to classify Hyperion hyperspectral data with 165 useable spectral bands. Results demonstrate that if parameters of the SVM, namely C and σ, are accurately adjusted, the SVM can be successfully used to identify alteration zones when field data samples are not available enough.
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نویسندگان مقاله امیر سلیمی |
faculty of mining, petroleum and geophysics, shahrood university of technology, shahrood, iran
سازمان اصلی تایید شده: دانشگاه صنعتی شاهرود (Shahrood university of technology)

منصور ضیایی |
faculty of mining, petroleum and geophysics, shahrood university of technology, shahrood, iran
سازمان اصلی تایید شده: دانشگاه صنعتی شاهرود (Shahrood university of technology)

مهدیه حسین جانی زاده | hosseinjani zadeh
department of ecology, institute of science and high technology and environmental science, graduate university of advanced technology, kerman, iran.
سازمان اصلی تایید شده: دانشگاه تحصیلات تکمیلی صنعتی کرمان (Graduate university of advanced technology)

علی امیری |
faculty of computer engineering, zanjan university, zanjan, iran
سازمان اصلی تایید شده: دانشگاه زنجان (Zanjan university)

صادق karimpouli |
mining engineering group, faculty of engineering, zanjan university, zanjan, iran
سازمان اصلی تایید شده: دانشگاه زنجان (Zanjan university)

نشانی اینترنتی http://ijmge.ut.ac.ir/article_56111_5ff8420bb8a661e8fa20a3b92e096b3e.pdf
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