气候变化领域集成服务门户(CCPortal): Multi-temporal RADARSAT-2 polarimetric SAR for maize mapping supported by segmentations from high-resolution optical image

CCPortal

DOI	10.1016/j.jag.2018.08.021
	Multi-temporal RADARSAT-2 polarimetric SAR for maize mapping supported by segmentations from high-resolution optical image
	Shuai G.; Zhang J.; Basso B.; Pan Y.; Zhu X.; Zhu S.; Liu H.
发表日期	2019
ISSN	15698432
起始页码	1
结束页码	15
卷号	74
英文摘要	Due to its ability to penetrate the cloud, Synthetic Aperture Radar (SAR) has been a great resource for crop mapping. Previous research has verified the applicability of SAR imagery in object-oriented crop classification, however, speckle noise limits the generation of optimal segmentation. This paper proposed an innovative SAR-based maize mapping method supported by optical image, Gaofen-1 PMS, based segmentation, named as parcel-based SAR classification assisted by optical imagery-based segmentation (os-PSC). Polarimetric decomposition was applied to extract polarimetric parameters from multi-temporal RADARSAT-2 data. One Gaofen-1 image was then used for parcel extraction, which was the basic unit for SAR image analysis. The final step was a multi-step classification for final maize mapping including: the potential maize mask extraction, pure/mixed maize parcel division and an integrated maize map production. Results showed that the overall accuracy of the os-PSC method was 89.1%, higher than those of pixel-level classification and SAR-based segmentation methods. The comparison between optical- and SAR-based segmentation demonstrated that optical-based segmentation would be better at representing maize field boundaries than the SAR-based segmentation. Moreover, the parcel- and pixel-level integrated classification will be suitable for many agricultural systems with small landownership where inter-cropping is common. Through integrating advantages of the SAR and optical data, os-PSC shows promising potentials for crop mapping. © 2018 Elsevier B.V.
英文关键词	Maize; Optical imagery; Parcel- and pixel-level integrated classification; PolSAR imagery
语种	英语
scopus关键词	image classification; image resolution; maize; noise; pixel; RADARSAT; satellite imagery; segmentation; speckle; synthetic aperture radar; vegetation mapping; Zea mays
来源期刊	International Journal of Applied Earth Observation and Geoinformation
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156546
作者单位	Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI 48824, United States; State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China; Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China; Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China; Beijing Polytechnic College, Beijing, 100042, China
推荐引用方式 GB/T 7714	Shuai G.,Zhang J.,Basso B.,et al. Multi-temporal RADARSAT-2 polarimetric SAR for maize mapping supported by segmentations from high-resolution optical image[J],2019,74.
APA	Shuai G..,Zhang J..,Basso B..,Pan Y..,Zhu X..,...&Liu H..(2019).Multi-temporal RADARSAT-2 polarimetric SAR for maize mapping supported by segmentations from high-resolution optical image.International Journal of Applied Earth Observation and Geoinformation,74.
MLA	Shuai G.,et al."Multi-temporal RADARSAT-2 polarimetric SAR for maize mapping supported by segmentations from high-resolution optical image".International Journal of Applied Earth Observation and Geoinformation 74(2019).