A survey of multiple classifier systems as hybrid systems <s> Change detection <s> Abstract In this paper, we propose a classification system based on a multiple-classifier architecture, which is aimed at updating land-cover maps by using multisensor and/or multisource remote-sensing images. The proposed system is composed of an ensemble of classifiers that, once trained in a supervised way on a specific image of a given area, can be retrained in an unsupervised way to classify a new image of the considered site. In this context, two techniques are presented for the unsupervised updating of the parameters of a maximum-likelihood classifier and a radial basis function neural-network classifier, on the basis of the distribution of the new image to be classified. Experimental results carried out on a multitemporal and multisource remote-sensing data set confirm the effectiveness of the proposed system. <s> BIB001 </s> A survey of multiple classifier systems as hybrid systems <s> Change detection <s> This paper addresses the problem of detecting land-cover transitions by analysing multitemporal remote-sensing images. In order to develop an effective system for the detection of land-cover transitions, an ensemble of non-parametric multitemporal classifiers is defined and integrated in the context of a multiple classifier system (MCS). Each multitemporal classifier is developed in the framework of the compound classification (CC) decision rule. To develop as uncorrelated as possible classification procedures, the estimates of statistical parameters of classifiers are carried out according to different approaches (i.e., multilayer perceptron neural networks, radial basis functions neural networks, and k-nearest neighbour technique). The outputs provided by different classifiers are combined according to three standard strategies extended to the compound classification case (i.e., Majority voting, Bayesian average, and Bayesian weighted average). Experiments, carried out on a multitemporal remote-sensing data set, confirm the effectiveness of the proposed system. <s> BIB002 </s> A survey of multiple classifier systems as hybrid systems <s> Change detection <s> In order to investigate the impacts of different information fusion techniques on change detection, a sequential fusion strategy combining pan-sharpening with decision level fusion is introduced into change detection from multi-temporal remotely sensed images. Generally, change map from multi-temporal remote sensing images using any single method or single kind of data source may contain a number of omission/commission errors, degrading the detection accuracy to a great extent. To take advantage of the merits of multi-resolution image and multiple information fusion schemes, the proposed procedure consists of two steps: (1) change detection from pan-sharpened images, and (2) final change detection map generation by decision level fusion. Impacts of different fusion techniques on change detection results are evaluated by unsupervised similarity metric and supervised accuracy indices. Multi-temporal QuickBird and ALOS images are used for experiments. The experimental results demonstrate the positive impacts of different fusion strategies on change detection. Especially, pan-sharpening techniques improve spatial resolution and image quality, which effectively reduces the omission errors in change detection; and decision level fusion integrates the change maps from spatially enhanced fusion datasets and can well reduce the commission errors. Therefore, the overall accuracy of change detection can be increased step by step by the proposed sequential fusion framework. <s> BIB003
Early application of MCS to land cover change detection was based on non-parametric algorithms, specifically MLP, k-NN, RBF, and ML classifiers BIB001 BIB002 , where classifier fusion was performed either by majority voting, Bayesian average and maximum a posteriori probability. Testing data were Thematic Mapper multispectral images, and the Synthetic Aperture Radar (SAR) of Landsat 5 satellite. Recent works on change detection in panchromatic images with MCS follow three different decision fuser strategies: majority voting, Dempster-Shafer evidence theory, and the Fuzzy Integral BIB003 . The sequential process of the images previous to classification includes pan-sharpening of the multi-temporal images, coregistration, raw radiometric change detection by image subtraction and automatic thresholding, and a final MCS decision computed on the multi-spectral data and the change detection data obtained from the various pan-sharpening approaches.