title: [Remote sensing techniques of apple orchard information extraction based on linear spectral unmixing with measured data].
abstract: Taking Qixia City, Shandong Province of China as the research region, and by using pixel unmixing for the TM image at apple flowering stage, the apple orchard information was extracted. Based on the measured spectral end-members, wavelet transform was adopted to improve the linear unmixing model. The improved linear spectral unmixing model, measured end-member based linear spectral unmixing model, and TM image end-member based linear spectral unmixing model were employed to extract the apple orchard information, and the ALOS data were used for accuracy estimation. After the accurate atmospheric and topographic correction, it was feasible to use the measured spectral end-members for pixel unmixing, and the area precision of apple orchard information acquisition was greater than 97%. The regression analysis on the NDVI of abundance image and the average NDVI of ALOS data showed that the R2 was higher than 0.8. Therefore, using wavelet transform to improve the linear spectral unmixing model could improve the unmixing accuracy to a certain degree.
authors.affiliation: College of Resources and Environment, Shandong Agricultural University, Tai'an 271018, Shandong, China. 2008smilefang@163.com
authors.forename: Fang
authors.initials: F
authors.lastname: Dong
authors.forename: Geng-Xing
authors.initials: GX
authors.lastname: Zhao
authors.forename: Ling
authors.initials: L
authors.lastname: Wang
authors.forename: Xi-Cun
authors.initials: XC
authors.lastname: Zhu
authors.forename: Chun-Yan
authors.initials: CY
authors.lastname: Chang
mesh_terms: D002681:China; D017753:Ecosystem; D016014:Linear Models; D027845:Malus / Q000254:growth & development*; D058998:Remote Sensing Technology / Q000379:methods*; D013057:Spectrum Analysis / Q000379:methods; D058067:Wavelet Analysis