DOI: 10.1016/j.jag.2014.09.019
Scopus记录号: 2-s2.0-84924424041
论文题名: Active extreme learning machines for quad-polarimetric SAR imagery classification
作者: Samat A ; , Gamba P ; , Du P ; , Luo J
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2015
卷: 35, 期: PB 起始页码: 305
结束页码: 319
语种: 英语
英文关键词: Active extreme learning machines
; Active learning
; Ensemble learning
; Extreme learning machine
; PolSAR
Scopus关键词: artificial intelligence
; image classification
; radar imagery
; synthetic aperture radar
英文摘要: Supervised classification of quad-polarimetric SAR images is often constrained by the availability of reliable training samples. Active learning (AL) provides a unique capability at selecting samples with high representation quality and low redundancy. The most important part of AL is the criterion for selecting the most informative candidates (pixels) by ranking. In this paper, class supports based on the posterior probability function are approximated by ensemble learning and majority voting. This approximation is statistically meaningful when a large enough classifier ensemble is exploited. In this work, we propose to use extreme learning machines and apply AL to quad-polarimetric SAR image classification. Extreme learning machines are ideal because of their fast operation, straightforward solution and strong generalization. As inputs to the so-called active extreme learning machines, both polarimetric and spatial features (morphological profiles) are considered. In order to validate the proposed method, results and performance are compared with random sampling and state-of-the-art AL methods, such as margin sampling, normalized entropy query-by-bagging and multiclass level uncertainty. Experimental results for four quad-polarimetric SAR images collected by RADARSAT-2, AirSAR and EMISAR indicate that the proposed method achieves promising results in different scenarios. Moreover, the proposed method is faster than existing techniques in both the learning and the classification phases. © 2014 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79511
Appears in Collections: 气候变化事实与影响
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作者单位: Key Lab. for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, China; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, China; Department of Electrical, Computer and Biomedical Engineering, University ofPavia, Italy
Recommended Citation:
Samat A,, Gamba P,, Du P,et al. Active extreme learning machines for quad-polarimetric SAR imagery classification[J]. International Journal of Applied Earth Observation and Geoinformation,2015-01-01,35(PB)