MAP-MRF Cloud Detection Based on PHD Filtering

被引：25

作者：

Addesso, Paolo ^{[1
]}

Conte, Roberto ^{[1
]}

Longo, Maurizio ^{[1
]}

Restaino, Rocco ^{[1
]}

Vivone, Gemine ^{[1
]}

机构：

[1] Univ Salerno, Dipartimento Ingn Elettron & Ingn Informat, I-84084 Fisciano, SA, Italy

来源：

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING | 2012年 / 5卷 / 03期

关键词：

Cloud masking; Markov Random Fields (MRF); Maximum A Posteriori estimation (MAP); Multi-Target Tracking (MTT); MSG SEVIRI; PHD filters; CLASSIFICATION; TRACKING; ALGORITHM; MOTION;

D O I：

10.1109/JSTARS.2012.2191144

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

080906 [电磁信息功能材料与结构]; 082806 [农业信息与电气工程];

摘要：

Temporal correlation has been recently taken into consideration to improve the performances of cloud detection algorithms. We exploit this concept within the Maximum A Posteriori Markov Random Field (MAP-MRF) framework by adding a penalty term which is determined according to the history of cloud masses. Multi Target Tracking of clouds is accomplished by methods of FInite Set STatistics (FISST) and several particle-based implementations are compared among them and with other previous methods both on simulated and real data.

引用

页码：919 / 929

页数：11

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