塔里木河下游土地利用覆被MISR多角度遥感制图

doi:10.13278/j.cnki.jjuese.201602306

吉林大学学报(地球科学版) ›› 2016, Vol. 46 ›› Issue (2): 617-626.doi: 10.13278/j.cnki.jjuese.201602306

• 地球探测与信息技术 • 上一篇

塔里木河下游土地利用覆被MISR多角度遥感制图

杨雪峰¹, 王雪梅^1,2, 毛东雷^1,2

1. 新疆师范大学地理科学与旅游学院, 乌鲁木齐 830054;
2. 新疆维吾尔自治区重点实验室"新疆干旱区湖泊环境与资源实验室", 乌鲁木齐 830054

收稿日期:2015-06-08 发布日期:2016-03-26
通讯作者: 王雪梅(1976-),女,副教授,博士,主要从事干旱区资源环境遥感技术应用研究,E-mail:502529672@qq.com E-mail:502529672@qq.com
作者简介:杨雪峰(1972-),男,讲师,硕士,主要从事干旱区资源环境遥感技术应用研究,E-mail:744157426@qq.com
基金资助:
国家自然科学基金项目(41261051);新疆维吾尔自治区重点实验室开放基金(XJDX0909-2010-08)

Mapping Land Use and Land Cover Through MISR Multi-Angle Imagery in the Lower Tarim River

Yang Xuefeng¹, Wang Xuemei^1,2, Mao Donglei^1,2

1. College of Geography Science and Tourism, Xinjiang Normal University, Urumqi 830054, China;
2. Xinjiang Uygur Autonomous Region Key laboratory "Xinjiang Laboratory of Lake Environment and Resources in Arid Zone", Urumqi 830054, China

Received:2015-06-08 Published:2016-03-26
Supported by:
Supported by the National Natural Science Foundation of China (41261051) and the Open Funds of Key Laboratory of Xinjiang Uygur Autonomous Region (XJDX0909-2010-08)

摘要/Abstract

摘要：

通过对塔里木河下游MISR卫星多角度观测数据的不同组合构建多角度数据集,探索多角度观测与传统垂直观测对土地利用覆被遥感制图效果的影响,分别使用SVM(支持向量机)与传统的MLC(最大似然分类法)作为分类器,对分类后得到的混淆矩阵进行分析。结论证实:无论是使用传统的MLC还是SVM作为分类器,多角度观测都取得比垂直观测更高的总体分类精度;MISR近红外波段虽然分辨率较低,但依然含有丰富的信息,对地表覆被的分类有重要影响;无论使用哪一数据集,SVM法都能获得更高的分类精度;不同相机对分类结果的影响各不相同,其中C、D相机的作用更重要。

关键词: MISR, 支持向量机, 最大似然分类法, 塔里木河下游, 土地利用覆被

Abstract:

MISR multi-angular data have been built through 9 cameras combination, and the infulence on land use and land cover mapping by multi-angular observing and traditional nadir approach has been explored in the lower Tarim River. In addition, SVM (support vector machine) and conventional MLC (maximum likelihood classification) were respectively implemented to observe the differentiation of Confusion Matrix. The findings are presented as follows:Multi-angular oberservation achieved higher classification accuracy compared to nadir approach no matter MLC or SVM classifiers being used; Although the lower resolution, MISR obtains abundent information, and thus has a great impact on the classification of vegetation;the classification by SVM shows a higher accuracy than that by MLC no matter which data set is used;different cameras lead to different results, but camera C and D paly more important roles than the others.

Key words: MISR, support vector machine, maximum lickelihood classification, lower Tarim River, land use and land cover

中图分类号:

P627

杨雪峰, 王雪梅, 毛东雷. 塔里木河下游土地利用覆被MISR多角度遥感制图[J]. 吉林大学学报(地球科学版), 2016, 46(2): 617-626.

Yang Xuefeng, Wang Xuemei, Mao Donglei. Mapping Land Use and Land Cover Through MISR Multi-Angle Imagery in the Lower Tarim River[J]. Journal of Jilin University(Earth Science Edition), 2016, 46(2): 617-626.

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塔里木河下游土地利用覆被MISR多角度遥感制图

Mapping Land Use and Land Cover Through MISR Multi-Angle Imagery in the Lower Tarim River

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