DOI: 10.1016/j.jag.2013.10.003
Scopus记录号: 2-s2.0-84897389586
论文题名: Integration of optical and synthetic aperture radar (SAR) images to differentiate grassland and alfalfa in Prairie area
作者: Hong G ; , Zhang A ; , Zhou F ; , Brisco B
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2014
卷: 28, 期: 1 起始页码: 12
结束页码: 19
语种: 英语
英文关键词: Image classification
; Image fusion
; Optical image
; SAR
Scopus关键词: alfalfa
; grassland
; image analysis
; image classification
; MODIS
; prairie
; radar imagery
; RADARSAT
; spatial distribution
; synthetic aperture radar
; Canada
英文摘要: Alfalfa presents a huge potential biofuel source in the Prairie Provinces of Canada. However, it remains a challenge to find an ideal single satellite sensor to monitor the regional spatial distribution of alfalfa on an annual basis. The primary interest of this study is to identify alfalfa spatial distribution through effectively differentiating alfalfa from grasslands, given their spectral similarity and same growth calendars. MODIS and RADARSAT-2 ScanSAR narrow mode were selected for regional-level grassland and alfalfa differentiation in the Prairie Provinces, due to the high frequency revisit of MODIS, the weather independence of ScanSAR as well as the large area coverage and the complementary characteristics SAR and optical images. Combining MODIS and ScanSAR in differentiating alfalfa and grassland is very challenging, since there is a large spatial resolution difference between MODIS (250 m) and ScanSAR narrow (50 m). This study investigated an innovative image fusion technique for combining MODIS and ScanSAR and obtaining a synthetic image which has the high spatial details derived from ScanSAR and the colour information from MODIS. The field trip was arranged to collect ground truth to label and validate the classification results. The fusion classification result shows significant accuracy improvement when compared with either ScanSAR or MODIS alone or with other commonly-used data combination methods, such as multiple files composites. This study has shown that the image fusion technique used in this study can combine the structural information from high resolution ScanSAR and colour information from MODIS to significantly improve the classification accuracy between alfalfa and grassland. © 2013 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79758
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Geography, York University, Toronto, ON M3J 1P3, Canada; GeoConnections and Canadian Geo-Secretariat, Natural Resources Canada, Ottawa, ON K1A 0E9, Canada; Canada Center for Remote Sensing, Ottawa, ON K1A 0Y7, Canada
Recommended Citation:
Hong G,, Zhang A,, Zhou F,et al. Integration of optical and synthetic aperture radar (SAR) images to differentiate grassland and alfalfa in Prairie area[J]. International Journal of Applied Earth Observation and Geoinformation,2014-01-01,28(1)