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Discriminant analysis of spatial data based on fusion of Markov random fields and machine learning

基于马尔可夫随机场与机器学习融合的空间数据判别分析

基本信息

批准号：
15540123
负责人：
NISHII Ryuei
金额：
$ 2.3万
依托单位：
KYUSHU UNIVERSITY (2004)Hiroshima University (2003)
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2004
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-15540123/
关键词：
AdaBoost Discriminant analysis Exponential loss function Image segmentation Machine learning Markov random fields Exponentiak loss function 学習理論判別分析 SVM アダブースト多重分光画像マイクロアレイデータ文字認識

项目摘要

Aims of the research were as follows : (a)Improvement of multispectral image classification due to machine learning based on spatial distributions of categories, (b)Derivation of selection method of machine learning approaches and model selection of Markov random fields, (c)Comparison of the proposed and the ordinary methods through actual satellite images. Hence the aims were to derive a fused classification method based on statistics and machine learning in short. Nishii and Eguchi (2004) proposed Spatial AdaBoost, which achieved the aims.Spatial AdaBoost is carried out in the following steps, (1)Obtain the posterior probabilities of the training data. (2)Calculate averages of log posteriors in neighborhoods of each pixels, and regard them as classification functions. (3)Tune the weights for the log posteriors by minimizing the exponential risk sequentially. (4)Classify test data by the convex combination of log posteriors.The proposed method is examined through simulated and real data sets, and it is seen that the method is very fast and shows a similar performance to MRF-based classifier. The method sometimes gives a negative weight for the log posterior for some case because the exponential loss puts a huge penalty for outlying misclassified data. Hence, Spatial AdaBoost based on robust loss functions is under investigation, and we obtain a partial answer.Further, we studied issues related to image analysis and machine learning, for example, robust loss functions and properties ; corrections of geometric and topographic effects ; morphological texture analysis ; and derivation of rotation invariant moments for character recognition.

研究的目的如下：（a）基于类别空间分布的机器学习改进多光谱图像分类;（B）推导机器学习方法的选择方法和马尔可夫随机场模型的选择;（c）通过实际卫星图像比较所提出的方法和普通方法。因此，我们的目标是得到一个融合的分类方法的基础上统计和机器学习。Nishii和Eguchi（2004）提出的Spatial AdaBoost算法实现了上述目标，Spatial AdaBoost算法的实现步骤如下：（1）获取训练数据的后验概率。（2）计算每个像元邻域内对数后验的平均值，并将其作为分类函数。（3）通过顺序地最小化指数风险来调整对数后验的权重。（4）利用对数后验的凸组合方法对测试数据进行分类，通过仿真数据和真实的数据集对该方法进行了检验，结果表明，该方法具有很好的快速性，并且与基于MRF的分类器性能相当。该方法有时会对某些情况下的对数后验给出负权重，因为指数损失会对离群误分类数据造成巨大损失。因此，基于鲁棒损失函数的空间AdaBoost正在研究中，我们得到了部分答案。进一步，我们研究了与图像分析和机器学习相关的问题，例如，鲁棒损失函数和性质;几何和地形效应的校正;形态纹理分析;以及用于字符识别的旋转不变矩的推导。