基于组合核非线性退化模型的遥感图像复合分类  

New compound classification method for remote sensing image based on multi-kernel non-linear regression model

作　　者：郭琳[1,2] 孙卫东[1] 王琼华[1] 杨邦杰[2] 

机构地区：[1]清华大学电子工程系,北京100084 [2]农业部规划设计研究院,北京100125

出　　处：《农业工程学报》2008年第10期145-150,F0003,共7页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金资助项目“基于多分辨率遥感图像的复合分类方法研究”(60472029)

摘　　要：遥感数据的多空间分辨率复合分析是遥感处理技术的重要发展方向。为了解决低分辨率图像混合像元分类精度低、高分辨率数据分类处理时间长以及大区域高分辨率数据获取困难等实际应用问题,该文改进了传统基于线性退化函数模型的复合分类模型,提出了基于组合核函数的非线性退化模型复合分类算法,分析了纹理信息对于提高复合分类精度的作用,并通过实际遥感数据试验分析比较了两种模型的分类精度。试验结果表明新方法可较大程度地提高总体分类精度,在分类过程中引入纹理信息有助于进一步改善分类精度。The integration and compound analysis of multi-resolution remote sensing image is one of the most important techniques in remote sensing image processing. For large scale land covering classification, it is common that low resolution data leads to worse performance due to the mixed-pixel problem, and high resolution data with wide covering range has more limitations such as long period of acquiring cycle, high data and processing cost. Facing these problems, an enhanced new compound classification method based on multi-kernel non-linear regression model was proposed, which could improve the description abilities of the traditional compound classification method based on linear regression model. Then, some texture information was introduced to improve the classification accuracy further more. Finally, the classification accuracies of the two regression models were compared through real data experiments based on the ground truth. The experimental results show that the classification accuracy is greatly improved by using this new method, and can be further improved with the texture information.

关 键 词：多空间分辨率遥感图像 复合分类 非线性退化模型 组合核函数 纹理 

分 类 号：TP72[自动化与计算机技术—检测技术与自动化装置]