An ROC analysis for subpixel detection

Chein I. Chang, Shao Shan Chiang, Qian Du, Hsuan Ren, Agustine Ifarragaerri

研究成果: 會議貢獻類型會議論文同行評審

37 引文 斯高帕斯(Scopus)


ROC (Receiver Operating Characteristic) analysis has been widely used to evaluate detection performance. It is based on the Neyman-Pearson detection theory, which solves binary hypothesis testing problems. In mixed pixel classification many algorithms that are developed to estimate abundance fractions of image endmembers generally produce gray scale images. As a result, they are not directly applied to hypothesis testing problems. Instead of using the standard ROC curve generated by the detection power versus the false alarm probability, a 3-dimensional (3-D) ROC curve is developed in this paper for subpixel detection. It is a 3-D plot derived from the mean-detection probability versus the mean-false alarm rate with the third dimension specified by abundance fractions produced by subpixel detection algorithms. In order to illustrate the utility of the proposed 3-D ROC analysis in subpixel detection, several linear unmixing-based algorithms are used for performance evaluation.

出版狀態已出版 - 2001
事件2001 International Geoscience and Remote Sensing Symposium (IGARSS 2001) - Sydney, NSW, Australia
持續時間: 9 7月 200113 7月 2001


???event.eventtypes.event.conference???2001 International Geoscience and Remote Sensing Symposium (IGARSS 2001)
城市Sydney, NSW


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