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首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >A Fast Cluster-Assumption Based Active-Learning Technique for Classification of Remote Sensing Images
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A Fast Cluster-Assumption Based Active-Learning Technique for Classification of Remote Sensing Images

机译：基于快速聚类假设的主动学习技术在遥感图像分类中的应用

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摘要

In this paper, we propose a simple, fast, and reliable active-learning technique for solving remote sensing image classification problems with support vector machine (SVM) classifiers. The main property of the proposed technique consists in its robustness to biased (poor) initial training sets. The presented method considers the 1-D output space of the classifier to identify the most uncertain samples whose labeling and inclusion in the training set involve a high probability to improve the classification results. A simple histogram-thresholding algorithm is used to find out the low-density (i.e., under the cluster assumption, the most uncertain) region in the 1-D SVM output space. To assess the effectiveness of the proposed method, we compared it with other active-learning techniques proposed in the remote sensing literature using multispectral and hyperspectral data. Experimental results confirmed that the proposed technique provided the best tradeoff among robustness to biased (poor) initial training samples, computational complexity, classification accuracy, and the number of new labeled samples necessary to reach convergence.
机译：在本文中，我们提出了一种简单，快速，可靠的主动学习技术，用于通过支持向量机（SVM）分类器解决遥感图像分类问题。所提出的技术的主要特性在于其对有偏差的（较差的）初始训练集的鲁棒性。提出的方法考虑了分类器的一维输出空间来识别最不确定的样本，这些样本的标记和包含在训练集中涉及提高分类结果的高概率。一种简单的直方图阈值算法用于找出一维SVM输出空间中的低密度（即在集群假设下，最不确定的）区域。为了评估该方法的有效性，我们将其与使用多光谱和高光谱数据的遥感文献中提出的其他主动学习技术进行了比较。实验结果证实，所提出的技术在对有偏差的（较差的）初始训练样本的鲁棒性，计算复杂性，分类准确性以及达到收敛所需的新标记样本的数量之间提供了最佳折衷。

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