DOI: 10.1016/j.jag.2013.03.002
Scopus记录号: 2-s2.0-84887308659
论文题名: Multi range spectral feature fitting for hyperspectral imagery in extracting oilseed rape planting area
作者: Pan Z ; , Huang J ; , Wang F
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2013
卷: 25, 期: 1 起始页码: 21
结束页码: 29
语种: 英语
英文关键词: Endmember
; Field-measured spectra
; Hyperspectral imagery
; Multi range spectral feature fitting
; Oilseed rape
Scopus关键词: accuracy assessment
; growth rate
; imagery
; mapping
; NDVI
; phenology
; plant
; software
; spectral analysis
; Brassica napus
; Hyperion
英文摘要: Spectral feature fitting (SFF) is a commonly used strategy for hyperspectral imagery analysis to discriminate ground targets. Compared to other image analysis techniques, SFF does not secure higher accuracy in extracting image information in all circumstances. Multi range spectral feature fitting (MRSFF) from ENVI software allows user to focus on those interesting spectral features to yield better performance. Thus spectral wavelength ranges and their corresponding weights must be determined. The purpose of this article is to demonstrate the performance of MRSFF in oilseed rape planting area extraction. A practical method for defining the weighted values, the variance coefficient weight method, was proposed to set up criterion. Oilseed rape field canopy spectra from the whole growth stage were collected prior to investigating its phenological varieties; oilseed rape endmember spectra were extracted from the Hyperion image as identifying samples to be used in analyzing the oilseed rape field. Wavelength range divisions were determined by the difference between field-measured spectra and image spectra, and image spectral variance coefficient weights for each wavelength range were calculated corresponding to field-measured spectra from the closest date. By using MRSFF, wavelength ranges were classified to characterize the target's spectral features without compromising spectral profile's entirety. The analysis was substantially successful in extracting oilseed rape planting areas (RMSE ≤ 0.06), and the RMSE histogram indicated a superior result compared to a conventional SFF. Accuracy assessment was based on the mapping result compared with spectral angle mapping (SAM) and the normalized difference vegetation index (NDVI). The MRSFF yielded a robust, convincible result and, therefore, may further the use of hyperspectral imagery in precision agriculture. © 2013 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79830
Appears in Collections: 气候变化事实与影响
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作者单位: Institute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou, 310029, China; Institute of Hydrology and Water Resources, Zhejiang University, Hangzhou 310058, China
Recommended Citation:
Pan Z,, Huang J,, Wang F. Multi range spectral feature fitting for hyperspectral imagery in extracting oilseed rape planting area[J]. International Journal of Applied Earth Observation and Geoinformation,2013-01-01,25(1)