Decomposition of SAR images’ mixed pixels based on supervised learning ICA algorithm

doi:10.11834/jrs.20090241

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Decomposition of SAR images’ mixed pixels based on supervised learning ICA algorithm
Vol. 13, Issue 2, Pages: 217-223(2009)
Published： 2009 ，
DOI： 10.11834/jrs.20090241

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[1]曹恒智,余先川,张立保.基于SL-ICA算法的SAR图像混合像元分解[J].遥感学报,2009,13(02):217-223.

Decomposition of SAR images’ mixed pixels based on supervised learning ICA algorithm. [J]. Journal of Remote Sensing 13(2):217-223(2009)
[1]曹恒智,余先川,张立保.基于SL-ICA算法的SAR图像混合像元分解[J].遥感学报,2009,13(02):217-223. DOI： 10.11834/jrs.20090241.

Decomposition of SAR images’ mixed pixels based on supervised learning ICA algorithm. [J]. Journal of Remote Sensing 13(2):217-223(2009) DOI： 10.11834/jrs.20090241.

摘要

为解决合成孔径雷达（SAR）图像存在大量混合像元的问题

针对传统ICA不能有效解决混合像元分解这一缺陷

提出一种新的独立成分分析算法——有监督学习ICA算法（SL-ICA）。其目标函数是在原ICA负熵目标函数基础上增加监督学习的约束条件项

进而在同一目标函数内实现负熵和约束条件的统一

在最大化负熵的同时也最小化了约束条件的误差

此外

采用一种新的双梯度下降法优化迭代

提高计算速度。并以人工模拟SAR图像和北京地区ENVISAT-ASAR作为数据源进行实验

实验结果明显优于主成分分析方法（PCA）的分解结果。

Abstract

For resolving the problem that there are lots of mixed pixels in the Synthetic Aperture Radar （SAR） images

against the flaw that the traditional Independent Component Analysis（ICA） can not solve the decomposition of mixed pixels effectively

we propose a new algorithm: Supervised Learning ICA algorithm（SL-ICA）. Adding supervised learning restrictive conditions to the negentropy objective function

we implement negentropy and restrictive conditions in a unified objective function

which minimizes the error while maximizing the negentropy. At the same time

we optimize the objective function using a new dual-gradient descent algorithm iteratively

which accelerates the computing speed. By testing SL-ICA and Principal Component Analysis （PCA）. on artificial simulated SAR images and ENVISAT-ASAR （Advanced Synthetic Aperture Radar） images of Beijing

the results show that SL-ICA can get more precise results than the PCA.

关键词

合成孔径雷达混合像元分解独立成分分析遥感影像主成分分析

Keywords

Synthetic Aperture Radar （SAR）decomposition of mixed pixelsIndependent Component Analysis （ICA）remote sensing imagePrincipal Component Analysis （PCA）

references

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Road extraction method of high-resolution remote sensing image on the basis of the spatial information perception semantic segmentation model

Review of deep learning in optical and SAR image fusion

Extracting glacier information from remote sensing imageries by automatic threshold method of Gaussian mixture model

Scene classification of remote sensing image using ensemble convolutional neural network

Research progress on methods of automatic coastline extraction based on remote sensing images

相关机构

School of Resources and Environmental Engineering, Anhui University

School of Resource and Environmental Science, Wuhan University

Anhui Engineering Research Center for Geographical Information Intelligent Technology

Faculty of Land and Resources Engineering, Kunming University of Science and Technology

Surveying and Mapping Geo-Informatics Technology Research Center on Plateau Mountains of Yunnan Higher Education

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