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首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >Multiscale Classification of Remote Sensing Images
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Multiscale Classification of Remote Sensing Images

机译：遥感影像的多尺度分类

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A huge effort has been applied in image classification to create high-quality thematic maps and to establish precise inventories about land cover use. The peculiarities of remote sensing images (RSIs) combined with the traditional image classification challenges made RSI classification a hard task. Our aim is to propose a kind of boost-classifier adapted to multiscale segmentation. We use the paradigm of boosting, whose principle is to combine weak classifiers to build an efficient global one. Each weak classifier is trained for one level of the segmentation and one region descriptor. We have proposed and tested weak classifiers based on linear support vector machines (SVM) and region distances provided by descriptors. The experiments were performed on a large image of coffee plantations. We have shown in this paper that our approach based on boosting can detect the scale and set of features best suited to a particular training set. We have also shown that hierarchical multiscale analysis is able to reduce training time and to produce a stronger classifier. We compare the proposed methods with a baseline based on SVM with radial basis function kernel. The results show that the proposed methods outperform the baseline.
机译：在图像分类中已进行了巨大的努力，以创建高质量的专题图并建立有关土地覆被使用的精确清单。遥感图像（RSI）的特殊性与传统的图像分类挑战相结合，使RSI分类成为一项艰巨的任务。我们的目的是提出一种适用于多尺度分割的升压分类器。我们使用增强的范式，其原理是结合弱分类器以构建有效的全局分类器。每个弱分类器都针对一级分割和一个区域描述符进行训练。我们已经提出并测试了基于线性支持向量机（SVM）和描述符提供的区域距离的弱分类器。实验是在咖啡种植园的大图像上进行的。我们已经在本文中表明，基于增强的方法可以检测最适合特定训练集的规模和特征集。我们还表明，分层多尺度分析能够减少训练时间并产生更强大的分类器。我们将提出的方法与基于带有径向基函数核的SVM的基线进行比较。结果表明，所提出的方法优于基线。

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