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Remote Sens. 2017, 9(2), 181; doi:10.3390/rs9020181

Specific Land Cover Class Mapping by Semi-Supervised Weighted Support Vector Machines

1
NOVA Information Management School, Universidade Nova de Lisboa, Lisboa 1070, Portugal
2
Direção Geral do Território, Lisboa 1070, Portugal
*
Author to whom correspondence should be addressed.
Academic Editors: Chandra Giri, Parth Sarathi Roy, Richard Gloaguen and Prasad S. Thenkabail
Received: 21 November 2016 / Revised: 26 January 2017 / Accepted: 15 February 2017 / Published: 21 February 2017
View Full-Text   |   Download PDF [4103 KB, uploaded 21 February 2017]   |  

Abstract

In many remote sensing projects on land cover mapping, the interest is often in a sub-set of classes presented in the study area. Conventional multi-class classification may lead to a considerable training effort and to the underestimation of the classes of interest. On the other hand, one-class classifiers require much less training, but may overestimate the real extension of the class of interest. This paper illustrates the combined use of cost-sensitive and semi-supervised learning to overcome these difficulties. This method utilises a manually-collected set of pixels of the class of interest and a random sample of pixels, keeping the training effort low. Each data point is then weighted according to its distance to its near positive data point to inform the learning algorithm. The proposed approach was compared with a conventional multi-class classifier, a one-class classifier, and a semi-supervised classifier in the discrimination of high-mangrove in Saloum estuary, Senegal, from Landsat imagery. The derived classification accuracies were high: 93.90% for the multi-class supervised classifier, 90.75% for the semi-supervised classifier, 88.75% for the one-class classifier, and 93.75% for the proposed method. The results show that accuracy achieved with the proposed method is statistically non-inferior to that achieved with standard binary classification, requiring however much less training effort. View Full-Text
Keywords: one-class support vector machines; weighted support vector machine; random training set; specific class mapping; land cover; mangrove; Landsat one-class support vector machines; weighted support vector machine; random training set; specific class mapping; land cover; mangrove; Landsat
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Silva, J.; Bacao, F.; Caetano, M. Specific Land Cover Class Mapping by Semi-Supervised Weighted Support Vector Machines. Remote Sens. 2017, 9, 181.

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