A General Framework for CFAR Detection in PolSAR Imagery Based on Quadratic Statistics
In the field of target detection in polarimetric synthetic aperture Radar (PolSAR) imagery, the constant false alarm rate (CFAR) algorithm is renowned for its operability and high interpretability. Given the challenges faced by deep learning methods in scenarios with limited labeled data and insuffi...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10945402/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850177749783674880 |
|---|---|
| author | Ziyuan Yang Liguo Liu Xiaoyang Hou Yinghui Quan Xian Zhang Tao Liu |
| author_facet | Ziyuan Yang Liguo Liu Xiaoyang Hou Yinghui Quan Xian Zhang Tao Liu |
| author_sort | Ziyuan Yang |
| collection | DOAJ |
| description | In the field of target detection in polarimetric synthetic aperture Radar (PolSAR) imagery, the constant false alarm rate (CFAR) algorithm is renowned for its operability and high interpretability. Given the challenges faced by deep learning methods in scenarios with limited labeled data and insufficient prior information, CFAR detection techniques remain vital in resource-constrained environments requiring rapid response. However, in the context of CFAR detection in PolSAR imagery, traditional intensity-based statistical modeling approaches, such as gamma distribution, generalized gamma distribution, and log-normal distribution, become inadequate when handling feature maps generated by nonpositive definite transformation matrices. In addition, some nonparametric methods suffer from a lack of robust statistical theoretical support, resulting in insufficient robustness. To effectively address this core issue, this study proposes a general framework for CFAR based on quadratic statistics, employing both the numerical solution of the Gil-Pelaez inversion formula and Monte Carlo reinforcement as strategies to determine detection thresholds. Experimental results from both simulated and real data demonstrate that this approach exhibits superior detection performance across diverse application scenarios, significantly improving the accuracy and robustness of target detection in PolSAR images. The key strength of this framework lies in providing a novel, unified solution for PolSAR image processing, effectively integrating the rigor of statistical analysis for practical applications. |
| format | Article |
| id | doaj-art-3cf14fd0f9fe49d483ad4bf06001aebc |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-3cf14fd0f9fe49d483ad4bf06001aebc2025-08-20T02:18:55ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118105141052910.1109/JSTARS.2025.355583310945402A General Framework for CFAR Detection in PolSAR Imagery Based on Quadratic StatisticsZiyuan Yang0https://orcid.org/0000-0001-7122-4173Liguo Liu1https://orcid.org/0000-0001-8461-164XXiaoyang Hou2Yinghui Quan3https://orcid.org/0000-0001-6541-9441Xian Zhang4Tao Liu5https://orcid.org/0000-0002-9596-4536School of Electronic Engineering, Naval University of Engineering, Wuhan, ChinaSchool of Electronic Engineering, Naval University of Engineering, Wuhan, ChinaSchool of Electronic Engineering, Naval University of Engineering, Wuhan, ChinaDepartment of Remote Sensing Science and Technology, School of Electronic Engineering, and the Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi'an, ChinaSchool of Electronic Engineering, Naval University of Engineering, Wuhan, ChinaSchool of Electronic Engineering, Naval University of Engineering, Wuhan, ChinaIn the field of target detection in polarimetric synthetic aperture Radar (PolSAR) imagery, the constant false alarm rate (CFAR) algorithm is renowned for its operability and high interpretability. Given the challenges faced by deep learning methods in scenarios with limited labeled data and insufficient prior information, CFAR detection techniques remain vital in resource-constrained environments requiring rapid response. However, in the context of CFAR detection in PolSAR imagery, traditional intensity-based statistical modeling approaches, such as gamma distribution, generalized gamma distribution, and log-normal distribution, become inadequate when handling feature maps generated by nonpositive definite transformation matrices. In addition, some nonparametric methods suffer from a lack of robust statistical theoretical support, resulting in insufficient robustness. To effectively address this core issue, this study proposes a general framework for CFAR based on quadratic statistics, employing both the numerical solution of the Gil-Pelaez inversion formula and Monte Carlo reinforcement as strategies to determine detection thresholds. Experimental results from both simulated and real data demonstrate that this approach exhibits superior detection performance across diverse application scenarios, significantly improving the accuracy and robustness of target detection in PolSAR images. The key strength of this framework lies in providing a novel, unified solution for PolSAR image processing, effectively integrating the rigor of statistical analysis for practical applications.https://ieeexplore.ieee.org/document/10945402/Constant false alarm rate (CFAR)Gil-Pelaez inversion formulaMonte Carlo reinforcementpolarimetric synthetic aperture radar (PolSAR)quadratic statistics |
| spellingShingle | Ziyuan Yang Liguo Liu Xiaoyang Hou Yinghui Quan Xian Zhang Tao Liu A General Framework for CFAR Detection in PolSAR Imagery Based on Quadratic Statistics IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Constant false alarm rate (CFAR) Gil-Pelaez inversion formula Monte Carlo reinforcement polarimetric synthetic aperture radar (PolSAR) quadratic statistics |
| title | A General Framework for CFAR Detection in PolSAR Imagery Based on Quadratic Statistics |
| title_full | A General Framework for CFAR Detection in PolSAR Imagery Based on Quadratic Statistics |
| title_fullStr | A General Framework for CFAR Detection in PolSAR Imagery Based on Quadratic Statistics |
| title_full_unstemmed | A General Framework for CFAR Detection in PolSAR Imagery Based on Quadratic Statistics |
| title_short | A General Framework for CFAR Detection in PolSAR Imagery Based on Quadratic Statistics |
| title_sort | general framework for cfar detection in polsar imagery based on quadratic statistics |
| topic | Constant false alarm rate (CFAR) Gil-Pelaez inversion formula Monte Carlo reinforcement polarimetric synthetic aperture radar (PolSAR) quadratic statistics |
| url | https://ieeexplore.ieee.org/document/10945402/ |
| work_keys_str_mv | AT ziyuanyang ageneralframeworkforcfardetectioninpolsarimagerybasedonquadraticstatistics AT liguoliu ageneralframeworkforcfardetectioninpolsarimagerybasedonquadraticstatistics AT xiaoyanghou ageneralframeworkforcfardetectioninpolsarimagerybasedonquadraticstatistics AT yinghuiquan ageneralframeworkforcfardetectioninpolsarimagerybasedonquadraticstatistics AT xianzhang ageneralframeworkforcfardetectioninpolsarimagerybasedonquadraticstatistics AT taoliu ageneralframeworkforcfardetectioninpolsarimagerybasedonquadraticstatistics AT ziyuanyang generalframeworkforcfardetectioninpolsarimagerybasedonquadraticstatistics AT liguoliu generalframeworkforcfardetectioninpolsarimagerybasedonquadraticstatistics AT xiaoyanghou generalframeworkforcfardetectioninpolsarimagerybasedonquadraticstatistics AT yinghuiquan generalframeworkforcfardetectioninpolsarimagerybasedonquadraticstatistics AT xianzhang generalframeworkforcfardetectioninpolsarimagerybasedonquadraticstatistics AT taoliu generalframeworkforcfardetectioninpolsarimagerybasedonquadraticstatistics |