Land cover classification using multi-temporal SAR data and optical data fusion with adaptive training sample selection
Land cover classification using multi-temporal SAR data and optical data fusion with adaptive training sample selection
复制标题
使用多时相 SAR 数据和光学数据融合以及自适应训练样本选择进行土地覆盖分类
DOI：
10.1109/igarss.2012.6352667
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
J. Susaki
中科院分区：
文献类型：
--
作者：
K. Chureesampant;J. Susaki
本文以单极化多时相合成孔径雷达（SAR）和光学数据为例，提出了基于贝叶斯理论的分类框架，并将所提出的训练样本选择（SS）方法进行了融合。在此框架下，结合基于灰度共生矩阵（GLCM）的平均纹理测度进行了研究。分类和提出的SS两个过程是统一的，其中SS生成准确和分散的训练样本。从多时相SAR数据中提取的特征，即平均后向散射系数、后向散射时间变率以及光学数据的长期相干性和反射率值，在框架中与GLCM平均纹理数据相结合。分类结果以日本大阪市为研究区域。选定的主要类别是建成区、田野、林地和水体。最适合用于分类的数据是多时相SAR和具有平均纹理的光学数据融合，因为使用的不同数据的补充和纹理图像的平滑效果。此外，使用SVM和基于神经网络的SS方法联合训练贝叶斯分类器获得的训练样本质量更高，在所有测试用例中产生的分类准确率最高。
This paper proposes the classification framework based on the Bayesian theory with the single polarization multi-temporal synthetic aperture radar (SAR) and an optical data, and incorporates the proposed training sample selection (SS) methods. Within this framework, the combination with gray level co-occurrence matrix (GLCM)-based mean textural measure is investigated. The two procedures of the classification and proposed SS are united, where SS generates accurate and dispersed training samples. Extracted features from multi-temporal SAR data-namely, the average backscattering coefficient, backscatter temporal variability, and long-term coherence and reflectance values from optical data, are integrated with GLCM mean textural data in the framework. Classification results were generated by taking Osaka City, Japan, as the study area. The selected major classes were built-up areas, fields, woodlands, and water bodies. The most suitable data used for classification is the multi-temporal SAR and an optical data fusion with the mean textural, because of the supplement of different data used and the smoothing effect on the images of the texture. Moreover, the higher quality training samples obtained by using the combined SVM and NN-based SS method for training the Bayesian classifier generated the highest classification accuracies in all of tested cases.