An Adaptive Thr esholding Multiple Classifiers System for Remote Sensing Image Classification

A multiple classifiers system which adopts an effective weighting policy to combine the output of several classifiers, generally leads to a better performance in image classification. The two most commonly used weighting policies are Bagging and Boosting algorithms. However, their performance is limited by high levels of ambiguity among classes. To overcome this difficulty, an adaptive thresholding criterion was proposed. By applying it to SAR and optical images for terrain cover classification, comparisons between the multiple classifiers systems using the Bagging and/or Boosting algorithms with and without the adaptive thresholding criterion were made. Experimental results showed that the classification substantially improved when the adaptive thresholding criterion was used, especially when the level of ambiguity of targets was high. Introduction A multiple classifiers system (MCS) which combines the output of several classifiers, generally leads to a better performance in a number of applications (Tumer and Ghosh, 1996; Kittler and Roli, 2000). Its aim is mainly to provide an effective merging method that takes advantage of each classifier while avoiding its weaknesses. Wolpert (1992) introduced the stacked generalization scheme for minimizing the classification error rate for one or more classifiers. The output from different classifiers is combined in a weighted sum with weights that are determined by the individual performance of the classifiers. Benediktsson et al. (1997) applied a parallel consensual neural network in classification/data fusion of remote sensing and geographic data. The input data were transformed several times, and the different transformed data were used as if they were independent inputs. Then, the independent inputs were classified, weighted, and combined to make a consensual decision. Serpico and Roli (1995) utilized a structured neural network in classification of remote sensing images. The structured neural network can be equivalently interpreted as a hierarchical arrangement of “committees” that accomplish the classification task by checking on a set of explicit constraints on input data. Carpenter et al. (1997) proposed the use of fuzzy ARTMAP neural networks for vegetation classification from PHOTOGRAMMETRIC ENGINEER ING & REMOTE SENS ING J u n e 2009 679 Yu-Chang Tzeng and Kou-Tai Fan are with the Department of Electronic Engineering, National United University, Miao-Li, Taiwan 360, ROC (john@nuu.edu.tw). Kun-Shan Chen is with the Center for Space and Remote Sensing Research, National Central University, Jhongli, Taiwan 320, ROC. Photogrammetric Engineering & Remote Sensing Vol. 75, No. 6, June 2009, pp. 679–687. 0099-1112/09/7506–0679/$3.00/0 © 2009 American Society for Photogrammetry and Remote Sensing An Adaptive Thr esholding Multiple Classifiers System for Remote Sensing Image Classification Yu-Chang Tzeng, Kou-Tai Fan, and Kun-Shan Chen a Landsat Thematic Mapper (TM) and terrain data. A voting strategy improves prediction and assigns confidence estimates by training the system several times on different orderings of an input set. Benediktsson and Swain (1992) utilized a consensus theory to combine single probability distributions to summarize estimates from multiple experts with the assumption that the experts had made decisions based on the Bayesian decision theory. Statistical analysis was augmented to include mechanisms weighting the influence of the data sources in the classification. Chanusso et al. (1999) presented a fusion method from detection results of multitemporal SAR images through fusion strategies to obtain a better discrimination between the good detections and the false alarms. Datcu et al. (2002) adopted the concept of dependence trees for the integration of information through estimation of probability distributions to apply a statistical approach to the classification of remote sensing data. An N th order binary distribution was approximated by a product of (N 1) second-order class conditional distributions. Then, a nonparametric method based on Gaussian kernels was used to estimate the secondorder class conditional distributions. Oza et al. (2005) gave recent developments of MCS. Fauvel et al. (2006) used a fuzzy decision rule to combine results of different classifiers. This was based on the definition of two measures of accuracy: a pointwise measure and the global accuracy of each classifier. Their results show a significant improvement in classification accuracy. For remote sensing applications, Benediktsson et al. (2007) present some recent developments of MCS. Some standard MCS methods (boosting, bagging, consensus theory, and random forests) are described and applied to multisource data for land-cover classification. Support vector machines (SVM) based algorithms are also described. More interestingly, the fusion of two classifiers using both spectral and spatial information is discussed in the frame of hyperspectral remote sensing for the classification of urban areas. In using multiple classifiers systems, it is expected that the classifiers, trained differently, will converge to local minima on the error plane and their overall performance will be improved by combining the individual outputs in one way or the other. Consider a single classifier that is trained according to a pre-selected training set. Let x be an input vector of the classifier, D denote the desired response, with F(x) the functional link between input and output, to 679–687_08-009.qxd 5/16/09 5:52 PM Page 679