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| 草地光谱分类最佳时相选择分析 | |
| 引用本文： | 张风丽,尹球,匡定波,李凤霞,周秉荣.草地光谱分类最佳时相选择分析[J].遥感学报,2006,10(4):482-488. |
| 作者姓名： | 张风丽 尹球 匡定波 李凤霞 周秉荣 |
| 作者单位： | 1. 中国科学院,上海技术物理研究所,上海,200083;中国科学院,遥感应用研究所,遥感科学国家重点实验室,北京,100101 2. 中国科学院,上海技术物理研究所,上海,200083 3. 青海省气象科学研究所,青海,西宁,810001 |
| 基金项目： | 国家自然科学基金;上海市科技发展基金;中国科学院知识创新工程项目 |
| 摘 要： | 利用2003年5-10月在环青海湖地区获取的典型天然草地与人工草地多时相地面高分辨率光谱数据，首先分析了最大似然分类法、支持向量机分类法、光谱角分类法、最小距离分类法和人工神经网络分类法所对应的最佳光谱变换方案；通过16个时相光谱数据的分类对比实验，分别确定了天然草地与人工草地分类、人工草地分类、天然草地分类的最佳时相；最后利用TM遥感数据对地面光谱数据分析结果进行了补充验证。 |
| 关 键 词： | 草地 反射光谱 时相 分类 |
| 文章编号： | 1007-4619(2006)04-0482-07 |
| 收稿时间： | 2004-09-16 |
| 修稿时间： | 2005-12-08 |
Optimal Temporal Selection for Grassland Spectrum Classification |
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| ZHANG Feng-li,YIN Qiu,KUANG Ding-bo,LI Feng-xia and ZHOU Bing-rong.Optimal Temporal Selection for Grassland Spectrum Classification[J].Journal of Remote Sensing,2006,10(4):482-488. | |
| Authors: | ZHANG Feng-li YIN Qiu KUANG Ding-bo LI Feng-xia and ZHOU Bing-rong |
| Institution: | Shanghai Institute of Technical Physics,CAS,Shanghai 200083,China;Institute of Remote Sensing Applications,Chinese Academy of Sciences,Beijing 100101,China;Shanghai Institute of Technical Physics,CAS,Shanghai 200083,China;Shanghai Institute of Technical Physics,CAS,Shanghai 200083,China;Meteorological Research Institute of Qinghai Province,Qinghai Xi'ning 810001,China;Meteorological Research Institute of Qinghai Province,Qinghai Xi'ning 810001,China |
| Abstract: | Grassland shows obvious seasonal patterns and the effect of grassland classification varies in different stage within its life span.Field spectrum data with high resolution of dominant grasslands in the region around Qinghai lake was collected at 16 temporals in 2003 using GER 1500 spectrometer.Analysis of spectrum classification experiments for grasslands shows that spectrum transformation affects the classification accuracy.Classification method combining with certain spectrum transformation can achieve much better result than using the raw spectrum reflectance.Maximum likelihood and support vector machine using moving average spectrum,spectral angle mapping and minimum distance using first-order derivative of spectrum's logarithm,and artificial neural network using first-order derivative of normalized spectrum can improve classification result.Then the paper carries out classification experiments for each temporal and determines the optimal temporal for grassland spectrum classification.The optimal temporal for natural and artificial grassland classification is at the beginning of grass turning green or in the middle ten days of August,with highest recognition accuracy mounting to 99%.The optimal temporal for artificial grassland classification is in the middle ten days of May,with highest recognition accuracy mounting to 95%,and it is worst for artificial grassland classification in the middle ten days of July.The optimal temporal for natural grassland classification is in the middle ten days of August,with highest recognition accuracy mounting to 87%.Experiment using TM data testifies the result derived from field spectrum data. |
| Keywords: | grassland reflectance spectrum temporal classification |
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