Journal
PROCEEDINGS OF FIFTH INTERNATIONAL CONFERENCE ON SOFT COMPUTING FOR PROBLEM SOLVING (SOCPROS 2015), VOL 2
Volume 437, Issue -, Pages 629-639Publisher
SPRINGER-VERLAG BERLIN
DOI: 10.1007/978-981-10-0451-3_56
Keywords
Fuzzy C-Means; Fuzzy possibilistic C-Means; K-means; Soft classification; Maximum likelihood classifier (MLC); Entropy
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Classification is a widely used technique for image processing and is used to extract thematic data for preparing maps in remote sensing applications. A number of factors affect the classification process. But classification is only half part of image processing and incomplete without accuracy assessment. Accuracy assessment of classification tells how accurately the classification process has been carried out. This research paper presents a review study of image classification through soft classifiers and also presents accuracy assessment of soft classifiers using entropy. Soft classifiers help in the development of more robust methods for remote sensing applications as compared to the hard classifiers. In this paper, two supervised soft classifiers, FCM, and PCM have been used to demonstrate the improvement in the classification accuracy by membership vector, RMSE, and also it has tried to generate fraction output from FCM, PCM, and noise with entropy.
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