| 基于缨帽变换与匹配滤波的大熊猫生境影像分类 |
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| 作者姓名： | 杨娅楠 习晓环 王成 王金亮 曾鸿程 |
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| 作者单位： | 1. 中国科学院遥感与数字地球研究所 数字地球重点实验室, 北京 100094;
2. 联合国教科文组织国际自然与文化遗产空间技术中心, 北京 100094;
3. 云南师范大学旅游与地理科学学院, 昆明 650500;
4. 加拿大多伦多大学地理与规划系, 多伦多 M5S 3G3 |
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| 基金项目： | 国家科技部国际科技合作专项(2013DFG21640)和中国科学院百人计划专项(09ZZ06101B)资助 |
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| 摘 要： | 在卧龙大熊猫自然保护区,难以获取高质量的光学遥感影像,加之地形和植被类型复杂多样,地物分类精度低,对利用遥感技术研究大熊猫生境造成了困难.为此,本工作探究一种提高遥感分类精度的方法.首先分别利用缨帽变换和匹配滤波方法提取影像的亮度、绿度、湿度和丰度等特征,建立基于多特征数据的决策树分类规则进行分类,最后利用野外实测数据对分类结果进行验证.研究结果表明:绿度特征对提取林地非常有效,湿度分量可以区分草甸与灌丛,亮度特征则提高了雪地的分类精度.匹配滤波可以实现混合像元分解,去除部分噪声并快速探测目标地物的波谱特征.基于多特征数据决策树遥感分类的总体精度达到83.33%,比传统的最大似然法分类精度提高8.67%.本文方法在卧龙大熊猫自然保护区等地物分类中取得了较好的应用效果.
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| 关 键 词： | 影像分类 缨帽变换 匹配滤波 决策树 大熊猫生境 |
| 收稿时间： | 2014-12-04 |
| 修稿时间： | 2015-05-20 |
Image classification for giant panda habitat using tasseled cap and matched filtering methods |
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| Authors: | YANG Yanan XI Xiaohuan WANG Cheng WANG Jinliang ZENG Hongcheng |
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| Institution: | 1. Key Laboratory of Digital Earth, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;
2. International Centre on Space Technologies for Natural and Cultural Heritage under the Auspices of UNESCO, Beijing 100094, China;
3. School of Tourism and Geographical Science, Yunnan Normal University, Kunming 650500, China;
4. Geography and Planning Department, University of Toronto, Toronto M5S 3G3, Canada |
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| Abstract: | It is difficult to acquire optical remote sensing images with good quality in Wolong nature reserve because of the weather conditions. Meanwhile, the terrain is very complex and the species are very rich, which leads to poor classification results when using Landsat TM data to study the panda's habitat. In this work, an effective classification method is proposed to improve classification accuracy. First, three features including greenness, brightness, and wetness are extracted from Landsat TM image by tasseled cap transformation. Second, the abundance index is deduced by matched filtering method. Then, the rules for decision tree are established by combining the results of the tasseled cap and matched filtering methods, and used to map the land use classification distribution in the study area. Finally, the classification results are validated by field measurements. The results show that the greenness is an effective measurement for extracting forest, wetness can distinguish between meadow and shrub, and brightness can improve the classification accuracy for snow. A matched filtering method for spectral unmixing removes image noise and effectively detects the spectra of targets. The overall accuracy of the decision tree classifier reaches 83.33%, which is 7.67% higher than that of the maximum likelihood classifier. This method improves the effectiveness and accuracy of image classification in Wolong nature reserve for giant pandas. |
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| Keywords: | image clssification tasseled cap matched filtering decision tree giant panda habitat |
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