| 高光谱遥感数据的DNA计算分类 |
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| 引用本文： | 焦洪赞,钟燕飞,张良培,李平湘.高光谱遥感数据的DNA计算分类[J].遥感学报,2010,14(5):872-885. |
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| 作者姓名： | 焦洪赞 钟燕飞 张良培 李平湘 |
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| 作者单位： | 武汉大学测绘遥感信息工程国家重点实验室,湖北武汉,430079 |
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| 基金项目： | 国家973计划资助项目(编号: 2009CB723905), 国家863计划资助项目(编号: 2009AA12Z114), 国家自然科学基金资助项目(编号: 40901213,40930532,40771139), 教育部博士点新教师基金(编号: 200804861058), 全国博士学位论文作者专项资金资助项目, 教育部新世纪优秀人才支持计划(编号: NECT-10-0624), 湖北省自然科学基金(编号: 2009CDB173), 模式识别国家重点实验室开放基金和地理空间信息工程国家测绘局重点实验室开 |
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| 摘 要： | 提出了一种基于DNA计算的高光谱遥感数据光谱匹配分类新方法。该方法利用DNA编码提取各类地物光谱所携带的物理吸收与反射特征信息,将地物光谱特征转换为DNA编码空间特征,通过DNA计算基因操作寻找各类地物最典型的DNA信息链。在此基础上,利用DNA计算原理建立一系列模糊规则,对高光谱数据进行光谱匹配分类。通过与传统的光谱匹配算法(二值编码,光谱角,光谱差分特征编码)的分类结果进行比较,证明该算法分类精度优于传统高光谱数据的光谱匹配分类方法,具有实用价值。
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| 关 键 词： | DNA计算 高光谱 光谱匹配 分类 |
| 收稿时间： | 2009/7/15 0:00:00 |
| 修稿时间： | 2009/11/11 0:00:00 |
Classification of hyperspectral remote sensing data based on DNA computing |
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| JIAO Hongzan,ZHONG Yanfei,ZHANG Liangpei and LI Pingxiang.Classification of hyperspectral remote sensing data based on DNA computing[J].Journal of Remote Sensing,2010,14(5):872-885. |
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| Authors: | JIAO Hongzan ZHONG Yanfei ZHANG Liangpei and LI Pingxiang |
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| Institution: | National Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Hubei Wuhan 430079, China;National Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Hubei Wuhan 430079, China;National Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Hubei Wuhan 430079, China;National Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Hubei Wuhan 430079, China |
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| Abstract: | Some initial investigations are conducted to employ DNA computing for hyperspectral remote sensing data classification. As a novel branch of computational intelligence, DNA computing expresses rich information of spectral features with DNA encoding, and acquires the most typical DNA encoding of each class by DNA modulating and controlling mechanism. For each pixel of the hyperspectral image, computing the distance between the pixel and the typical DNA sequence, finding the class property of the minimum distance, set the class property of each pixel as the minimum distance class. An experiment was performed to evaluate the performance of the proposed algorithm in comparison with other traditional image matching classification algorithms: binary cording, spectral angles and spectral derivative feature coding (SDFC). It is demonstrated that the proposed algorithm is superior to the three traditional hyperspectral data classification algorithms based on the experiment results. |
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| Keywords: | DNA computation hyperspectral spectral matching classification |
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