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首页> 外文会议>Pixels, Objects, Intelligence: GEOgraphic Object Based Image Analysis for the 21st Century >AUTOMATIC CLASSIFICATION OF CENTRAL ITALY LAND COVER: COMPARATIVE ANALYSIS OF ALGORITHMS
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AUTOMATIC CLASSIFICATION OF CENTRAL ITALY LAND COVER: COMPARATIVE ANALYSIS OF ALGORITHMS

机译：意大利中央陆地覆盖自动分类：算法比较分析

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The specific objective of this paper was to provide a comparative analysis of three automatic classification algorithms: Quinlan's C4.5 and two robust probabilistic classifiers like Support Vector Machine (SVM) and AdaBoost (a short for "adaptive boosting"). This work is part of a wider project whose general objective is to develop a methodology for the automatic classification, based on CORINE land-cover (CLC) classes, of high resolution multispectral IKONOS images. The dataset used for the comparison is an area of approximately 150 km~(2) comprising both urban and rural environments. Input data are basically constituted by multispectral (red, green, blue and infrared bands), 4m ground-resolution images. In some classifications they are integrated by the Normalized-Difference-Vegetation-Index (NVDI), derived from the red and infrared bands, a Digital Terrain Model (DTM) of the area and pixel by pixel gradient values, derived by the DTM. All the above algorithms had to perform full data classification into four classes: vegetation, water bodies, bare soil, and artificial cover. The output is constituted by an image with each pixel assigned to one of the above classes or, with the exception of C4.5, let unclassified (somehow a better solution than a classification error). In addition, a confusion matrix for control data is produced to evaluate the accuracy of each algorithm, by computing the percentage of correctly classified pixels with respect to the total number of pixels, the user's and producer's accuracy indexes and the Cohen's coefficient to evaluate global accuracy. Even if an optimal distribution of the samples in the training set has a great influence, results demonstrate the suitability of supervised classifiers for high resolution land cover classification. In particular, all the proposed approaches work fine, so that we are now exploring the use of more classes, that is at the second level of the CORINE legend.
机译：本文的具体目标是提供三种自动分类算法的比较分析：昆兰的C4.5和两个强大的概率分类器，如支持向量机（SVM）和Adaboost（简短的“自适应升压”）。这项工作是更广泛的项目的一部分，其一般目标是基于Corine MultiSpectral Ikonos图像的冠状覆盖（CLC）类来开发自动分类方法。用于比较的数据集是包含城市和农村环境的大约150公里〜（2）的面积。输入数据基本上由多光谱（红色，绿色，蓝色和红外带），4M地分辨率图像构成。在某些分类中，它们由归一化 - 差异 - 植被指数（NVDI）集成，来自红色和红外条带，该区域的数字地形模型（DTM）和由DTM导出的像素梯度值。所有上述算法都必须将完整的数据分类为四类：植被，水体，裸土和人造盖。输出由具有分配给上述类别之一的每个像素的图像构成，或者除C4.5之外，不完整的（以某种方式比分类错误更好的解决方案）。此外，通过计算相对于像素总数，用户和生产者的准确性指标和科恩的系数来评估正确分类的像素的百分比来评估每个算法的困境以评估每种算法的准确性。 。即使训练集中样品的最佳分布具有很大的影响，结果也表明了监督分类器对高分辨率陆地覆盖分类的适用性。特别是，所有提议的方法都罚款，因此我们现在正在探索更多课程，这是在康鱼传奇的第二级。

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