摘要
针对多源遥感影像分类的需要,本文提出一种光学-极化SAR影像特征融合与分类,利用山东省泰安市区域的Landsat-5与全极化ALOSPALSAR卫星进行了实验验证。实验结果表明,引入全极化SAR目标分解后向散射特征与极化均值纹理特征的特征级影像融合分类,总体精度达到98.8048%,能够充分利用影像特征之间的合作性与互补性,减少分类结果的椒盐噪声,从而有效地提高影像的分类精度。
To make full use of the multi-source remote sensing images for classification, a new method was proposed based on features fusion and classification of optical and full-polarization Advanced Land Observing Satellite-the Phased Array Type L-band Synthetic Aperture Radar (ALOS PALSAR) images.The Landsat-5 and full-polarization ALOS PALSAR satellite in Taian City of Shandong province are used to verify the results of the experiments. The experiment result shows that the overall accuracy reached to 98.804 8% when the full-polarization SAR target decomposition backscattering features and polarmetric mean texture feature are introduced, which can make full use of the cooperation and complimentarily between the image features, reduce the salt and pepper noise and effectively improve the classification accuracy.
作者
苏瑞雪
汤玉奇
SU Ruixue;TANG Yuqi(School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha 410083, China)
出处
《测绘与空间地理信息》
2019年第6期51-55,共5页
Geomatics & Spatial Information Technology
基金
国家自然科学基金资助项目(51479215)
湖南省自然科学基金资助项目(2015JJ3150)
湖南省地理国情监测项目(HNGQJC201503)
国家高分专项高分遥感测绘应用示范系统项目(AH1601-8)
湖南省教育厅创新平台开放基金(101595)资助
关键词
全极化SAR
光谱特征
纹理特征
融合
分类
full-polarization SAR
spectral feature
texture feature
fusion
classification
