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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >A Feature-Space Indicator Kriging Approach for Remote Sensing Image Classification
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A Feature-Space Indicator Kriging Approach for Remote Sensing Image Classification

机译：遥感图像分类的特征空间指标克里金法

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摘要

An indicator kriging (IK) approach for remote sensing image classification is proposed. By introducing indicator variables for categorical data, the work of image classification is transformed into estimation of class-dependent probabilities in feature space using ordinary kriging. Individual pixels are then assigned to the class with maximum class probability. The approach is distribution free and yields perfect classification accuracies for training data provided that collocated data in feature space do not exist. Technical considerations regarding implementation of IK such as indicator semivariogram modeling and handling of collocated data in feature space are also described. The IK, Gaussian-based maximum likelihood, nearest neighbor, and support vector machine (SVM) classifiers were applied to study areas within the Shimen reservoir watershed (case A: FORMOSAT-2) and Taipei city (case B: SPOT 4). The results show that the overall accuracies of the proposed IK classifier and SVM can achieve higher than 97% for training data and 81% for testing data. (The overall accuracies of IK are a little higher than those of SVM.) IK and SVM are found to be superior to the other two classifiers in terms of overall accuracies for both training and testing data. The proposed IK classifier has the following advantages: 1) It can deal with anisotropic problem in feature space; 2) it is a nonparametric method and needs not to know the type of probability distribution; and 3) it yields 100% classification accuracy for the training data provided that collocated data in feature space do not exist.
机译：提出了一种用于遥感影像分类的指示器克里金（IK）方法。通过引入分类数据的指标变量，使用普通克里金法将图像分类的工作转换为特征空间中与类相关的概率的估计。然后，以最大的分类概率将各个像素分配给该分类。该方法是无分布的，并且在特征空间中不存在并置数据的情况下，可以为训练数据提供完美的分类精度。还介绍了有关IK实现的技术注意事项，例如指标半变异函数建模和特征空间中并置数据的处理。将IK，基于高斯的最大似然，最近邻和支持向量机（SVM）分类器应用于石门水库集水区（案例A：FORMOSAT-2）和台北市（案例B：SPOT 4）内的区域。结果表明，所提出的IK分类器和SVM的总体精度可以达到97％以上的训练数据和81％的测试数据。 （IK的总体准确度比SVM的略高。）在训练和测试数据的总体准确度方面，发现IK和SVM优于其他两个分类器。提出的IK分类器具有以下优点：1）它可以处理特征空间中的各向异性问题； 2）这是一种非参数方法，不需要知道概率分布的类型； 3）如果特征空间中不存在并置数据，则训练数据的分类精度为100％。

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