DOI: 10.1016/j.jag.2016.06.014
Scopus记录号: 2-s2.0-84997785029
论文题名: Fusion of HJ1B and ALOS PALSAR data for land cover classification using machine learning methods
作者: Wang X ; Y ; , Guo Y ; G ; , He J ; , Du L ; T
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2016
卷: 52 起始页码: 192
结束页码: 203
语种: 英语
英文关键词: ALOS/PALSAR
; HJ1B
; Image fusion
; Land cover classification
Scopus关键词: algorithm
; ALOS
; image classification
; land cover
; machine learning
; PALSAR
; pixel
; satellite imagery
英文摘要: Image classification from remote sensing is becoming increasingly urgent for monitoring environmental changes. Exploring effective algorithms to increase classification accuracy is critical. This paper explores the use of multispectral HJ1B and ALOS (Advanced Land Observing Satellite) PALSAR L-band (Phased Array type L-band Synthetic Aperture Radar) for land cover classification using learning-based algorithms. Pixel-based and object-based image analysis approaches for classifying HJ1B data and the HJ1B and ALOS/PALSAR fused-images were compared using two machine learning algorithms, support vector machine (SVM) and random forest (RF), to test which algorithm can achieve the best classification accuracy in arid and semiarid regions. The overall accuracies of the pixel-based (Fused data: 79.0%; HJ1B data: 81.46%) and object-based classifications (Fused data: 80.0%; HJ1B data: 76.9%) were relatively close when using the SVM classifier. The pixel-based classification achieved a high overall accuracy (85.5%) using the RF algorithm for classifying the fused data, whereas the RF classifier using the object-based image analysis produced a lower overall accuracy (70.2%). The study demonstrates that the pixel-based classification utilized fewer variables and performed relatively better than the object-based classification using HJ1B imagery and the fused data. Generally, the integration of the HJ1B and ALOS/PALSAR imagery can improve the overall accuracy of 5.7% using the pixel-based image analysis and RF classifier. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80063
Appears in Collections: 气候变化事实与影响
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作者单位: State Key Laboratory Breeding Base of Land Degradation and Ecological Restoration of Northwest China, Ningxia University, Yinchuan, Ningxia, China; Key Laboratory for Restoration and Reconstruction of Degraded Ecosystem in Northwestern China of Ministry of Education, Ningxia University, Yinchuan, Ningxia, China; School of Resources and Environment, Ningxia University, Yinchuan, Ningxia, China
Recommended Citation:
Wang X,Y,, Guo Y,et al. Fusion of HJ1B and ALOS PALSAR data for land cover classification using machine learning methods[J]. International Journal of Applied Earth Observation and Geoinformation,2016-01-01,52