遥感影像投影寻踪回归分类模型

张正健; 李祚泳; 秦宁生; 刘志红; 巴桑

doi:10.11834/jig.20121216

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专辑

遥感影像投影寻踪回归分类模型
Remote sensing image classification model based on projection pursuit regression
2012年17卷第12期页码：1568-1574
纸质出版：2012
DOI： 10.11834/jig.20121216
稿件说明：

移动端阅览

张正健, 李祚泳, 秦宁生, 刘志红, 巴桑. 遥感影像投影寻踪回归分类模型[J]. 中国图象图形学报, 2012,17(12):1568-1574. DOI： 10.11834/jig.20121216.

Zhang Zhengjian, Li Zuoyong, Qin Ningsheng, Liu Zhihong, Ba Sang. Remote sensing image classification model based on projection pursuit regression[J]. Journal of Image and Graphics, 2012, 17(12): 1568-1574. DOI： 10.11834/jig.20121216.

摘要

将投影寻踪回归分析技术引入遥感影像分类中

详尽叙述遥感影像投影寻踪回归分类模型的建立和实现过程。将广州地区的TM影像用于分类实验

并用混合蛙跳算法来优化投影寻踪回归分类模型中的参数矩阵

取得了较为理想的分类效果。此外

还进一步分析了投影中心的设定、调整以及优化算法和岭函数个数对投影寻踪回归模型分类精度的影响。实验结果表明

该模型易于优化实现

稳定性强

模型中岭函数的个数对投影寻踪回归模型的分类精度没有显著影响。

Abstract

We introduce the projection pursuit regression (PPR)analysis for remote sensing image classification and describe the implementation of the PPR model used in remote sensing image classification. Using a TM image of the Guangzhou area for our classification tests

we get satisfactory classification result

after optimizing the parameters in the projection pursuit regression model by shuffled frog leaping algorithm. Furthermore

we discuss the setting of the projection center as well as the influence of the optimal algorithms and the number of ridge functions on the classification accuracy in the PPR model. The results show that the model is easy to realize and very stable.The number of ridge functions in the projection pursuit regression model has no significant influence on the classification accuracy.