Introduction In the past decade, a number of papers 19,28 have proposed the combination of multiple classifiers for designing high performance pattern classification systems. The rationale behind the growing interest in multiple classifier systems (MCSs) is that the classical approach to designing a pattern recognition system, which focuses on the search for the best individual classifier, has some serious drawbacks. The main drawback is that the best individual classifier for the classification task at hand is very di#cult to identify, 199 200 F. Roli & G. Giacinto unless deep prior knowledge is available for such a task. 3,8 In addition, with a single classifier it is not possible to exploit the complementary discriminatory information that other classifiers may encapsulate. It is worth noting that the motivations in favour of MCS strongly resemble those of a "hybrid" intelligent system. 15,23 The obvious reason for this is that MCS can be regarded as a special-purpose hy

We review more than two hundred applications of neural networks in image processing and discuss the present and possible future role of neural networks, especially feed-forward neural networks, Kohonen feature maps and Hopfield neural networks. The various applications are categorised into a novel two-dimensional taxonomy for image processing algorithms. One dimension specifies the type of task performed by the algorithm: preprocessing, data reduction/feature extraction, segmentation, object recognition, image understanding and optimisation. The other dimension captures the abstraction level of the input data processed by the algorithm: pixel-level, local feature-level, structurelevel, object-level, object-set level and scene characterisation. Each of the six types of tasks poses specific constraints to a neural-based approach. These specific conditions are discussed in detail. A synthesis is made of unresolved problems related to application of pattern recognition techniques in image processing and specifically to the application of neural networks. Finally, we present an outlook into the future application of neural networks and relate them to novel developments. Keywords: neural networks; digital image processing; invariant pattern recognition; preprocessing; feature extraction; image compression; segmentation; object recognition; image understanding; optimization. * Corresponding author. M. Egmont-Petersen, Institute of Information and Computing Sciences, Utrecht University, P.O.B. 80.089, 3508 TB Utrecht, The Netherlands. Email: michael@cs.uu.nl. WWW: Http://www.cs.uu.nl/people/michael/nn-review.html.

Recently, the concept of "Multiple Classifier Systems" vas proposed as a nev approach to the development of high performance image classification systems. Multiple Classifier Systems can be used to improve classification accuracy by combining the outputs of classifiers making "uncorrelated" errors. Unfortunately, in real image recognition problems, it may be very difficult to design an ensemble of classifiers that satisfies this assumption. In this paper, ve propose a different approach based on the concept of "adaptive selection" of multiple classifiers in order to select the most appropriate classifier for each input pattern.

Land usage classification is an essential part of many remote sensing applications for mapping, inventory, and yield estimation. In this contribution, we evaluate the potential of the recently introduced support vector machines for remote sensing applications. Moreover, we expand this discriminative technique by a novel Bayesian approach to estimate the confidence of each classification. These estimates are combined with a priori knowledge about topological relations of class labels using a contextual classification step based on the iterative conditional mode algorithm (ICM). As shown for Landsat TM imagery, this strategy is highly competitive and outperforms several commonly used classification schemes.

Abstract—In remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of the pixel intensities. This paper deals with the problem of probability density function (pdf) estimation in the context of synthetic aperture radar (SAR) amplitude data analysis. Several theoretical and heuristic models for the pdfs of SAR data have been proposed in the literature, which have been proved to be effective for different land-cover typologies, thus making the choice of a single optimal parametric pdf a hard task, especially when dealing with heterogeneous SAR data. In this paper, an innovative estimation algorithm is described, which faces such a problem by adopting a finite mixture model for the amplitude pdf, with mixture components belonging to a given dictionary of SAR-specific pdfs. The proposed method automatically integrates the procedures of selection of the optimal model for each component, of parameter estimation, and of optimization of the number of components by combining the stochastic expectation–maximization iterative methodology with the recently developed “method-of-log-cumulants ” for parametric pdf estimation in the case of nonnegative random variables. Experimental results on several real SAR images are reported, showing that the proposed method accurately models the statistics of SAR amplitude data. Index Terms—Finite mixture models (FMMs), parametric estimation, probability density function (pdf) estimation, stochastic expectation maximization (SEM), synthetic aperture radar (SAR) images. I.

Abstract—In the context of remotely sensed data analysis, an important problem is the development of accurate models for the statistics of the pixel intensities. Focusing on synthetic aperture radar (SAR) data, this modeling process turns out to be a crucial task, for instance, for classification or for denoising purposes. In this paper, an innovative parametric estimation methodology for SAR amplitude data is proposed that adopts a generalized Gaussian (GG) model for the complex SAR backscattered signal. A closed-form expression for the corresponding amplitude probability density function (PDF) is derived and a specific parameter estimation algorithm is developed in order to deal with the proposed model. Specifically, the recently proposed “method-of-log-cumulants ” (MoLC) is applied, which stems from the adoption of the Mellin transform (instead of the usual Fourier transform) in the computation of characteristic functions and from the corresponding generalization of the concepts of moment and cumulant. For the developed GG-based amplitude model, the resulting MoLC estimates turn out to be numerically feasible and are also analytically proved to be consistent. The proposed parametric approach was validated by using several real ERS-1, XSAR, E-SAR, and NASA/JPL airborne SAR images, and the experimental results prove that the method models the amplitude PDF better than several previously proposed parametric models for backscattering phenomena. Index Terms—Generalized Gaussian (GG), parametric estimation, probability density function (PDF), synthetic aperture radar (SAR). I.

We develop an approach for segmenting radiographic images of focal bone lesions possibly caused by bone tumour. A neural network is used to classify individual pixels by a convolution operation based on a feature vector. We design eight features chat characterise the local texture in the neighbourhood of a pixel. Four of the features are based on co occurrence matrices computed from the neighbourhood. The true class label of the pixels in the radiographs are obtained from annotations made by an experienced radiologist. We make a comparison of several statistical classifiers based on different criteria and argue that neural networks are most suited for our application. Feed-forward neural networks and self-organising feature maps are trained to perform the segmentation cask. The experiments confirm che feasibility of using a feature-based neural network for finding pathologic bone changes in radiographic images. An analysis of the eight features indicates that the presence of edges and transitions, the complexity of the texture, as well as the amount of high frequencies in the texture, are the main features discriminating (soft) tissue from pathologic bone, the two classes most likely to be confused.

In 1999, the NASA Jet Propulsion Lab presented a proposal for a six satellite navigation and communication network for Mars called the Mars Network. This thesis investigates the performance of the Mars Network both theoretically, using figures of merit commonly applied to satellite navigation systems on Earth, and in the position domain using simulated observations.

In the field of pattern recognition, the combination of an ensemble of neural networks has been proposed as an approach to the development of high performance image classification systems. However, previous work clearly showed that such image classification systems are effective only if the neural networks forming them make different errors. Therefore, the fundamental need for methods aimed to design ensembles of "error-independent" networks is currently acknowledged. In this paper, an approach to the automatic design of effective neural network ensembles is proposed. Given an initial large set of neural networks, our approach is aimed to select the subset formed by the most error-independent nets. Reported results on the classification of multisensor remote-sensing images show that this approach allows one to design effective neural network ensembles.

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