基于实测光谱混合像元分解的苹果园地遥感提取技术

应用生态学报

基于实测光谱混合像元分解的苹果园地遥感提取技术

董芳^1,2,赵庚星^1**,王凌¹,朱西存¹,常春艳¹

（¹山东农业大学资源与环境学院, 山东泰安 271018; ²济南大学资源与环境学院, 济南 250022）

出版日期:2012-12-18 发布日期:2012-12-18

Remote sensing techniques of apple orchard information extraction based on linear spectral unmixing with measured data.

DONG Fang^1,2, ZHAO Geng-xing¹, WANG Ling¹, ZHU Xi-cun¹, CHANG Chun-yan¹

(¹College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, Shandong, China; ²College of Resources and Environment, University of Ji’nan, Ji’nan 250022, China)

Online:2012-12-18 Published:2012-12-18

摘要/Abstract

摘要： 以山东省栖霞市为研究区,对苹果花期的TM影像进行混合像元分解,提取苹果园地信息.基于实测地物光谱端元,利用小波变换对线性分解模型进行改进,采用实测端元改进后线性分解模型、实测端元线性分解模型、TM影像端元线性分解模型分别提取研究区苹果园地信息,并以ALOS数据进行精度评价.结果表明: 经过精确的大气及地形校正后,可以利用实测光谱端元进行混合像元分解,面积精度＞97%,对丰度图像的归一化植被指数（NDVI）值与ALOS数据的平均NDVI值进行回归分析,R²＞0.8;利用小波变换对线性分解模型进行改进,可在一定程度上提高分解精度.

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.

董芳1,2,赵庚星1**,王凌1,朱西存1,常春艳1. 基于实测光谱混合像元分解的苹果园地遥感提取技术[J]. 应用生态学报.

DONG Fang1,2, ZHAO Geng-xing1, WANG Ling1, ZHU Xi-cun1, CHANG Chun-yan1. Remote sensing techniques of apple orchard information extraction based on linear spectral unmixing with measured data.[J]. Chinese Journal of Applied Ecology.