文章基本信息

标题：Enteromorpha Prolifera Detection with MODIS Image Using Semi-supervised Clustering
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作者：Wu, Shunyao ; Shao, Fengjing ; Wang, Ying 等
期刊名称：Journal of Computers
印刷版ISSN：1796-203X
出版年度：2014
卷号：9
期号：5
页码：1259-1265
DOI：10.4304/jcp.9.5.1259-1265
语种：English
出版社：Academy Publisher
摘要：In recent years, enteromorpha prolifera detectionhas received increasing attention. Supervised learning withremote sensing images can achieve satisfactory performancesfor green tide monitoring. However, data distributions betweenimages obviously differ, and it would be too costlyto label a massive amount of images for enteromorphaprolifera detection. Thus, this paper focuses on detectingenteromorpha prolifera using not only limited labelled data,but also a large amount of unlabelled data. We propose aneffective semi-supervised clustering framework for enteromorphaprolifera detection, which can reduce the labellingcost and alleviate the overfitting problem. Experimentalresults prove the effectiveness and potential of our approach,with almost a 15% increase from baseline. In addition, theproposed approach can provide quantitative assessments forband data of moderate resolution imaging spectroradiometer(MODIS) images, and several often ignored bands, such asbands 5, 6, and 7, are shown to be useful for enteromorphaprolifera detection.
关键词：enteromorpha detection;semi-supervised clustering;remote sensing images