Consider a confusion matrix obtained by a classifier of land-cover categories. Usually, misclassification rates are not uniformly distributed in off-diagonal elements of the matrix. Some categories are easily classified from the others, and some are not. The loss function used by AdaBoost ignores the difference. If we derive a classifier which is efficient to classify categories close to the remaining categories, the overall accuracy may be improved. In this paper, the exponential loss function with different costs for
misclassification is proposed in multiclass problems. Costs due to misclassification should be pre-assigned. Then, we obtain an emprical cost risk function to be minimized, and the minimizing procedure is established (Cost AdaBoost). Similar treatments for logit loss functions are discussed. Also, Spatial Cost AdaBoost is proposed. Out purpose is originally to minimize the expected cost. If we can define costs appropriately, the costs are useful for reducing error rates. A simple numerical example shows that the proposed method is useful for reducing error rates.
We propose a contextual unsupervised classification method of geostatistical data based on combination of Ward clustering method and Markov random fields (MRF). Image is clustered into classes by using not only spectrum of pixels but also spatial information. For the classification of remote sensing data of low spatial resolution, the treatment of mixed pixel is importance. From the knowledge that the most of mixed pixels locate in boundaries of land-covers, we first detect edge pixels and remove them from the image. We here introduce a new measure of spatial adjacency of the classes. Spatial adjacency is used to MRF-based update of the classes. Clustering of edge pixels are processed as final step. It is shown that the proposed method gives higher accuracy than conventional clustering method does.
In category classification of remotely sensed imagery, it is important that pixels of image are classified using spatial informaton. We have implemented MRF(Markov Random Field) model for a classification of higher accuracy. The model of MRF is a random field whose random variable is owed to its neighborhood. The LANDSAT TM data of the Kanto area, Japan, has been alalyzed with the manner of iteration in which probability density function for a confiuration of classes reaches a maximum. Partly because of taking into account of edge information in image, the results show considerably good classification.