Supervised image classification by contextual AdaBoost based on posteriors in neighborhoods
Supervised image classification by contextual AdaBoost based on posteriors in neighborhoods
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DOI：
10.1109/tgrs.2005.848693
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发表时间：
2005-10
影响因子：
8.2
通讯作者：
R. Nishii;S. Eguchi
中科院分区：
文献类型：
--
作者：
R. Nishii;S. Eguchi
AdaBoost是一种机器学习技术，用于对地质统计数据的土地覆盖类别进行监督分类。我们引入了基于邻域像素的上下文分类器。首先，在所有像素上计算后验概率。然后，在不同的邻域中计算原木后方的平均值，并将其用作上下文分类函数。分类函数的权重可以通过最小化多类的经验风险来确定。最后，得到了分类函数的一个凸组合。分类是通过非迭代最大化过程来执行的。将该方法应用于人工多光谱图像和基准数据集。该方法性能优良，与基于马尔可夫随机场的分类器相似，后者需要迭代最大化过程。
AdaBoost, a machine learning technique, is employed for supervised classification of land-cover categories of geostatistical data. We introduce contextual classifiers based on neighboring pixels. First, posterior probabilities are calculated at all pixels. Then, averages of the log posteriors are calculated in different neighborhoods and are then used as contextual classification functions. Weights for the classification functions can be determined by minimizing the empirical risk with multiclass. Finally, a convex combination of classification functions is obtained. The classification is performed by a noniterative maximization procedure. The proposed method is applied to artificial multispectral images and benchmark datasets. The performance of the proposed method is excellent and is similar to the Markov-random-field-based classifier, which requires an iterative maximization procedure.