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Mapping raised bogs with an iterative one-class classification approach

机译：使用迭代的一类分类方法映射凸起的沼泽

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摘要

Land use and land cover maps are one of the most commonly used remote sensing products. In many applications the user only requires a map of one particular class of interest, e.g. a specific vegetation type or an invasive species. One-class classifiers are appealing alternatives to common supervised classifiers because they can be trained with labeled training data of the class of interest only. However, training an accurate one-class classification (OCC) model is challenging, particularly when facing a large image, a small class and few training samples. To tackle these problems we propose an iterative OCC approach. The presented approach uses a biased Support Vector Machine as core classifier. In an iterative pre-classification step a large part of the pixels not belonging to the class of interest is classified. The remaining data is classified by a final classifier with a novel model and threshold selection approach. The specific objective of our study is the classification of raised bogs in a study site in southeast Germany, using multi-seasonal RapidEye data and a small number of training sample. Results demonstrate that the iterative OCC outperforms other state of the art one-class classifiers and approaches for model selection. The study highlights the potential of the proposed approach for an efficient and improved mapping of small classes such as raised bogs. Overall the proposed approach constitutes a feasible approach and useful modification of a regular one-class classifier. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
机译：土地利用和土地覆盖图是最常用的遥感产品之一。在许多应用中，用户仅需要一个特定兴趣类别的地图，例如特定的植被类型或入侵物种。一类分类器是常见的受监管分类器的吸引人的替代方法，因为它们只能使用感兴趣类别的标记训练数据进行训练。但是，训练准确的一类分类（OCC）模型是一项挑战，特别是当面对大图像，小类和少量训练样本时。为了解决这些问题，我们提出了一种迭代OCC方法。提出的方法使用偏差支持向量机作为核心分类器。在迭代预分类步骤中，不属于关注类别的大部分像素被分类。最终分类器使用新颖的模型和阈值选择方法对其余数据进行分类。我们研究的特定目标是使用多季节的RapidEye数据和少量训练样本对德国东南部某研究地点的沼泽进行分类。结果表明，迭代OCC优于其他现有的一类分类器和模型选择方法。这项研究突出了所提出的方法对于有效和改进小类（如凸起的沼泽）的绘图的潜力。总体而言，所提出的方法构成了可行的方法，并对常规的一类分类器进行了有用的修改。 （C）2016国际摄影测量与遥感学会（ISPRS）。由Elsevier B.V.发布。保留所有权利。

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