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| 结合SOM神经网络和混合像元分解的高光谱影像分类方法研究 | |
| 引用本文： | 徐宏根,马洪超,李德仁.结合SOM神经网络和混合像元分解的高光谱影像分类方法研究[J].遥感学报,2007,11(6):778-786. |
| 作者姓名： | 徐宏根 马洪超 李德仁 |
| 作者单位： | 1. 武汉大学,遥感信息工程学院,湖北,武汉,430079 2. 武汉大学,遥感信息工程学院,湖北,武汉,430079;武汉大学,测绘遥感信息工程国家重点实验室,湖北,武汉,430079 3. 武汉大学,测绘遥感信息工程国家重点实验室,湖北,武汉,430079 |
| 基金项目： | 国家高技术研究发展计划(863计划);教育部国防基础科研项目 |
| 摘 要： | 本文对SOM神经网络算法进行改进,在标类的过程中采用3个策略加以控制,对初始产生的自组织映射图进行调整。通过改进,那些映射到可靠神经元的像素得到了很好的分类,而那些映射到不可靠神经元的像素都被作为不可分像元而提取出来。继而,从混合像元分解的角度来对这些不可分像元进行处理,按类型分解的思想确定混合像元的类别,实现对不可分像元的分类。将SOM神经网络和混合像元分解相结合的分类方法应用于高光谱图像的分类中,通过实验表明了该方法能较好地改善分类效果,提高分类精度。 |
| 关 键 词： | 高光谱影像分类 SOM神经网络 混合像元分解 |
| 文章编号： | 1007-4619（2007）06-0778-09 |
| 修稿时间： | 2005-12-16 |
Research on the Classification Based on SOM and LSMA for Hyperspectral Image |
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| XU Hong-gen,MA Hong-chao and LI De-ren.Research on the Classification Based on SOM and LSMA for Hyperspectral Image[J].Journal of Remote Sensing,2007,11(6):778-786. | |
| Authors: | XU Hong-gen MA Hong-chao and LI De-ren |
| Institution: | 1. School of Remote Sensing and Information Engineering, Wuhan University, Hubei Wuhan 430079, China ; 2. National Key Lab for Information Engineering in Surveying , Mapping and Remote Sensing, Wuhan University, Hubei Wuhan 430079, China |
| Abstract: | In SOM algorithm it will create a map in output layer in which the cells are labeled class ID,e.g.1,2,3,etc.It's curial for correctly classifying the data to make map.In this paper,we focus our interests on analyzing the map and the process of creating map to improve the SOM.We take three measures to change the map.We can classify the pure pixels and find the mixed pixels through the changed map.Furthermore,we can process the unclassified pixels from the view of linear spectral mixture analysis(LSMA).Furthermore,we consider the two constraints: unnegative and the sum one,so the constraint spectral mixture analysis(CSMA) is applied in this paper.After CSMA,we assign the class ID to the endmember which has largest proportion in the mixed pixel.So,the spectral unmixing classification based on category proportion is performed to the unclassified pixels.Thus,we can get the extreme classification combining the former results.The experiment shows that the classification combined SOM with LSMA can get better classification results and well improve the classification accuracy. |
| Keywords: | hyperspectral image SOM neural network linear spectral mixture analysis(LSMA) |
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