An Effective Evaluation Tool for Hyperspectral Target Detection: 3D Receiver Operating Characteristic Curve Analysis

By Chein-I. Chang. IEEE Transactions on Geoscience and Remote Sensing, 2021

319 citations recorded by the source corpus.

Receiver operating characteristic (ROC) analysis is performed by a curve, called ROC curve, plotted based on detection probability, <inline-formula> <tex-math notation="LaTeX">$P_{\text {D}}$ </tex-math></inline-formula>, versus false alarm probability, <inline-formula> <tex-math notation="LaTeX">$P_{\text {F}}$ </tex-math></inline-formula>, and has been widely used as an evaluation tool for signal detection. Specifically, the area under an ROC curve (AUC) is calculated and used as a detection measure. Unfortunately, finding distributions of <inline-formula> <tex-math notation="LaTeX">$P_{\text {D}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$P_{\text {F}}$ </tex-math></inline-formula> to generate a continuous ROC curve is practically infeasible. This article investigates approaches to generating a discrete 2D ROC curve of (<inline-formula> <tex-math notation="LaTeX">$P_{\text {D}}$ </tex-math></inline-formula>,<inline-formula> <tex-math notation="LaTeX">$P_{\text {F}}$ </tex-math></inline-formula>) without appealing for probability distributions. Since <inline-formula> <tex-math notation="LaTeX">$P_{\text {D}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$P_{\text {F}}$ </tex-math></inline-formula> are determined by the same threshold <inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> to specify a detector, an ROC curve of (<inline-formula> <tex-math notation="LaTeX">$P_{\text {D}}$ </tex-math></inline-formula>,<inline-formula> <tex-math notation="LaTeX">$P_{\text {F}}$ </tex-math></inline-formula>) can only be used to evaluate the effectiveness of a detector but not target detectability (TD) and also not background suppressibility (BS). To address this issue, a 3D ROC curve is generated as a function of (<inline-formula> <tex-math notation="LaTeX">$P_{\text {D}}$ </tex-math></inline-formula>,<inline-formula> <tex-math notation="LaTeX">$P_{\text {F}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula>) by introducing a specific threshold parameter <inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> as a third independent variable. As a result, a 3D ROC curve along with its derived three 2D ROC curves of (<inline-formula> <tex-math notation="LaTeX">$P_{\text {D}}$ </tex-math></inline-formula>,<inline-formula> <tex-math notation="LaTeX">$P_{\text {F}}$ </tex-math></inline-formula>), (<inline-formula> <tex-math notation="LaTeX">$P_{\text {D}}$ </tex-math></inline-formula>,<inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula>), and (<inline-formula> <tex-math notation="LaTeX">$P_{\text {F}}$ </tex-math></inline-formula>,<inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula>) can further be used to design new quantitative measures to evaluate the effectiveness of a detector and its TD and BS. To demonstrate the full utility of 3D ROC analysis in target detection, extensive experiments are performed on two types of targets, prior target detection and anomaly detection, to conduct a comprehensive analysis on 3D ROC curves using new designed detection measures to evaluate target/anomaly detection performance.

Subfields: Artificial Intelligence, Media Technology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c583459ed68d2d22b4080e1759a26d3784ed7480

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In Artificial Intelligence

1. Lina Zhuang
2. Antonio J. Plaza
3. Degang Wang
4. Xu Sun
5. Min Huang
6. Lianru Gao

In Media Technology

1. Lina Zhuang
2. Lianru Gao
3. Antonio J. Plaza
4. Degang Wang
5. Xu Sun
6. Min Huang
7. José M. Bioucas-Dias
8. Jocelyn Chanussot

In Radiology, Nuclear Medicine and Imaging

1. Antonio J. Plaza
2. Degang Wang
3. Xu Sun
4. Min Huang
5. Lianru Gao
6. Lina Zhuang

Citations in the corpus

• BockNet: Blind-Block Reconstruction Network With a Guard Window for Hyperspectral Anomaly Detection (2023, methodology): “2) Evaluation Metrics: To assess the detection performance of the different compared approaches, some well-known metrics are adopted, that is, the receiver-operating characteristic (ROC) curve, the area under the ROC curve (AUC) [72], and the statistical separability map.”