지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2014.2
- 수록면
- 34 - 43 (10page)
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초록· 키워드
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Classification techniques are used on large databases to develop models describing different data classes. Such analysis can provide deep insight for better understanding of different large-scale databases. Studies related to knowledge acquisition and effective knowledge development are also very popular in the remote sensing field with satellite imagery datasets. In any remote sensing research, the decision-making process mainly depends on the effectiveness of the classification process. Efficient classification techniques were developed and applied to the Statlog (Landsat Satellite) database at the University of California, Irvine Machine Learning Repository to identify six land type classes. We used three different classification algorithms on the large satellite imagery: multilayer perceptron backpropagation neural network (MLP BPNN), support vector machine (SVM), and k-nearest neighbor (k-NN). This research study aimed to evaluate the performance of these classification algorithms in the prediction of the classified lands from this large set of satellite imagery. We used different performance measures, such as classification accuracy, root-mean-square error, kappa statistic, true positive rate, false positive rate, precision, recall, and F-measure to evaluate the performance of each classifier. Among the three classification techniques applied, MLP BPNN turned out to be the best; next was k-NN, followed by SVM.
목차
Abstract
Introduction
Related Work
Methodology
Methodology
Conclusion
References
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UCI(KEPA) : I410-ECN-0101-2015-500-002466791