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首页> 外文会议>Asian Conference on Remote Sensing >COMPARISON OF SUPPORT VECTOR MACHINES, RANDOM FOREST AND DECISION TREE METHODS FOR CLASSIFICATION OF SENTINEL - 2A IMAGE USING DIFFERENT BAND COMBINATIONS
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COMPARISON OF SUPPORT VECTOR MACHINES, RANDOM FOREST AND DECISION TREE METHODS FOR CLASSIFICATION OF SENTINEL - 2A IMAGE USING DIFFERENT BAND COMBINATIONS

机译：使用不同频带组合对Sentinel - 2A图像分类的支持向量机，随机林和决策树方法的比较

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Classification of remotely sensed images is a prerequisite for many earth observation studies including change detection, yield forecast and water quality analysis. Recent studies showed that machine learning algorithms used for the classification of satellite image high accurate results. In this study, three popular machine learning algorithms namely, random forest (RF), support vector machines (SVM) and decision tree (DT) classifiers were utilized considering three datasets that comprise different band combinations of a Sentinel-2A image. These datasets consist of five, seven and eleven bands containing an image of normalized difference vegetation index (NDVI). In the classification process, six land use/cover classes covering the bulk of the study area were determined as forest, grass, asphalt road, soil and bare area, urban and water. In the classification stage, 700 pixels for training and 300 pixels for testing were selected for each class to avoid possible bias among the classes. Classification resulted revealed that SVM classifier produced the best accuracy results for all three datasets. The highest accuracy (95.17%) was achieved with SVM classifier using the 11-band combination dataset. The combinations containing high spatial resolution bands provided higher accuracies. Moreover, McNemar's test was applied to analyze statistical significance of classifier performances for the datasets. Also F-score test was applied for all class to evaluate classification accuracy results. The results indicated that the differences between the performances were statistically significant except for SVM and RF using 7-band and 11-band combinations. To sum up, the efficiency of the machine learning algorithms applied in this study were all found effective in classification of Sentinel-2A imagery.
机译：远程感测图像的分类是许多地球观察研究的先决条件，包括改变检测，产量预测和水质分析。最近的研究表明，机器学习算法用于卫星图像的分类高准确的结果。在本研究中，考虑三个数据集，三个流行的机器学习算法包括随机森林（RF），支持向量机（SVM）和决策树（DT）分类器，其包括Sentinel-2a图像的不同频带组合。这些数据集包含五个，七个和十一带，其中包含归一化差异植被指数（NDVI）的图像。在分类过程中，涵盖大部分研究区域的六种土地使用/覆盖类被确定为森林，草，沥青路，土壤和裸露的地区，城市和水。在分类阶段，为每个类选择用于训练的700像素和300像素进行测试，以避免类中可能的偏置。分类结果表明，SVM分类器为所有三个数据集产生了最佳准确性结果。使用11频段组合数据集，使用SVM分类器实现了最高精度（95.17％）。包含高空间分辨率带的组合提供了更高的精度。此外，麦克马尔的测试应用于分析数据集的分类器性能的统计显着性。对于所有课程，还应用了F分测试以评估分类准确性结果。结果表明，除了使用7波段和11波段组合的SVM和RF外，性能之间的差异是统计学意义的。总而言之，本研究中应用的机器学习算法的效率均有效地在Sentinel-2a图像的分类中有效。

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