主讲人：Chein-I Chang, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, USA
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Chein-I Chang 教授于1987年毕业于美国马里兰大学帕克分校，电机工程专业博士。现任美国马里兰大学巴尔地摩郡分校电机工程系终身教授，同时也是IEEE Life Fellow与SPIE Fellow、大连海事大学讲座教授、台湾中兴大学遥测科技杰出讲座教授，并担任IEEE Transaction on Geoscience and Remote Sensing、Remote Sensing等多个国际知名期刊编委，已发表SCI检索学术论文200余篇，其中超过100篇属于高光谱领域，撰写高光谱领域专著4部，并编著高光谱领域书籍3部，授权美国专利7项，Google被引次数达25000多次，Google学术H指数为66。
Hyperspectral target detection can be performed in two different modes, active detection such as known target detection and passive detection such as anomaly detection. To evaluate detection performance, a general criterion is to use the area under a receiver operating characteristic (ROC) curve, AUC which is plotted based on detection probability, PD versus false alarm probability, PF. Unfortunately, Unfortunately, many ROC curves reported in the literature are indeed incorrectly generated. Another major issue is that using AUC of a ROC curve of (PD,PF), denoted by AUC(D,F) is unreliable and misleading because PD and PF are generated by the same threshold. As a result, a higher PD also generates a higher PF and vice versa. To address these two issues this talk presents a 3D ROC analysis which generates a 3D ROC curve as a function of (PD,PF,) by including the threshold parameter as a third independent variable. Consequently, a 3D ROC curve along with its derived three 2D ROC curves of (PD,PF), (PD,) and (PF,) can be further used to design new quantitative measures to evaluate the effectiveness of a detector and its target detectability TD and background suppressibility (BS). To demonstrate the full utility of 3D ROC analysis in target detection, examples are included in this talk to demonstrate how 3D ROC curves can be used to design new detection measures to evaluate target/anomaly detection performance more effectively ad accurately in terms, TD, BS and detector’s effectiveness.