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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Non-Parametric Object-Based Approaches to Carry Out ISA Classification From Archival Aerial Orthoimages
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Non-Parametric Object-Based Approaches to Carry Out ISA Classification From Archival Aerial Orthoimages

机译：基于非参数对象的方法从档案航空正射影像中进行ISA分类

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摘要

In order to map the impervious surfaces for a coastal area, three non-parametric approaches: Classification and Regression Trees, Nearest Neighbor (NN), and Support Vector Machines (SVM)- were applied to a dataset of very high resolution archival orthoimages which had poor radiometry, with only red, green and blue spectral information. An object-based image analysis was carried out and four feature vectors were defined as input data for the classifier: 1) red, green and blue spectral information plus four relative spectral indices; 2) Dataset 1 plus texture indices based on the grey level co-occurrence matrix (GLCM); 3) Dataset 1 plus texture indices based on the local variance; and 4) the vector defined by 1), 2) and 3). Two classification strategies were developed in order to identify the pervious/impervious target classes (aggregation of all the subclasses and binary classification). The separability matrix was used to present the statistical comparative results clearly and concisely. The results obtained from this work showed that 1) “GLCM” texture indices did not lead to more accurate results; 2) the incorporation of the local variance texture index significantly increased the accuracy of the classification; 3) the classification results were not significantly affected by the classification strategy employed; 4) SVM and NN achieved statistically more accurate classification results than CARTs; 5) the SVM classifier was more efficient than the NN classifier, while NN was less dependent on the feature vector, and 6) suitable accuracy results were obtained for the most accurate approaches (SVM) which achieved a 89.4% overall accuracy.
机译：为了绘制沿海地区的不透水表面，将三种非参数方法：分类和回归树，最近邻（NN）和支持向量机（SVM）-应用于具有高分辨率档案正射影像的数据集辐射度差，仅具有红色，绿色和蓝色光谱信息。进行了基于对象的图像分析，并将四个特征向量定义为分类器的输入数据：1）红色，绿色和蓝色光谱信息加上四个相对光谱索引； 2）数据集1加上基于灰度共生矩阵（GLCM）的纹理索引； 3）数据集1加上基于局部方差的纹理索引； 4）由1），2）和3）定义的向量。为了识别可渗透/不可渗透的目标类别（所有子类别的汇总和二元分类），开发了两种分类策略。可分离性矩阵用于清晰简洁地呈现统计比较结果。从这项工作中获得的结果表明：1）“ GLCM”纹理指数并未导致更准确的结果； 2）纳入局部方差纹理指数显着提高了分类的准确性； 3）分类结果不受所采用分类策略的显着影响； 4）SVM和NN在统计上比CARTs更准确； 5）SVM分类器比NN分类器更有效，而NN较少依赖特征向量，并且6）最准确的方法（SVM）获得了合适的准确度结果，总体准确度达到89.4％。

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