Combining Parametric and Non-parametric Algorithms for a Partially Unsupervised Classification of Multitemporal Remote-Sensing Images

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 (ML) classifier and a radial basis function (RBF) 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

A Contrast-Based Approach to the Identification of Texture Faults

Texture analysis based on the extraction of contrast features is very effective in terms of both computational complexity and discrimination capability. In this framework, max-min approaches have been proposed in the past as a simple and powerful tool to characterize a statistical texture. In the present work, a method is proposed that allows exploiting the potential of max -min approaches to efficiently solve the problem of detecting local alterations in a uniform statistical texture. Experimental results show a high defect discrimination capability and a good attitude to real-time applications, which make it particularly attractive for the development of industrial visual inspection systems

Also: to be published on Information Fusion Combining Parametric and Non-parametric Algorithms for a Partially Unsupervised

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 (ML) classifier and a radial basis function (RBF) 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

Method for extracting information of interest from multi-dimensional, multi-parametric and/or multi-temporal datasets

Method of extraction of information of interest to multi-dimensional, multi-parametric and/or multi-temporal datasets related to a same object under observation through data fusion, in which a plurality of different data sets are provided concerning a single object, with the data related to various parameters and/or different time acquisition instants of said parameters. The data set are subjected to a first processing step by principal component analysis generated by an identical number of datasets with transformed data; and each of the datasets is combined in non-linearly with the corresponding transformed data set to obtain a certain predetermined number of combinations of parameters by weighing using parameters determined empirically using training datasets which determine the values of the non-linear weighting parameters that maximize the value of the new features associated with the data of interest, as \u202

A neural network-based image processing system for detection of vandal acts in unmanned railway environments

In the last years, the interest for advanced video-based surveillance applications is more and more growing. This is especially true in the field of railway urban transport where video-based surveillance can be exploited to face many relevant security aspects (e.g. vandal acts, overcrowding situations, abandoned object detection, etc.). This paper 1 aims at investigating an open problem in the implementation of video-based surveillance systems for transport applications, i.e.: the implementation of reliable image understanding modules in order to recognize dangerous situations with reduced false alarm and misdetection rates. In this work, we considered the use of a neural network-based classifier for detecting vandal behaviors in metro stations. The achieved results show that the classifier choice mentioned above allows one to achieve very good performances also in presence of high scene complexity. 1

Multisensorial Vision For Autonomous Vehicle Driving

A multisensorial vision system for autonomous vehicle driving is presented, that operates in outdoor natural environments. The system, currently under development in our laboratories, will be able to integrate data provided by different sensors in order to achieve a more reliable description of a scene and to meet safety requirements. We chose to perform a high-level symbolic fusion of the data to better accomplish the recognition task. A knowledge-based approach is followed, which provides a more accurate solution; in particular, it will be possible to integrate both physical data, furnished by each channel, and different fusion strategies, by using an appropriate control structure. The high complexity of data integration is reduced by acquiring, filtering, segmenting and extracting features from each sensor channel. Production rules, divided into groups according to specific goals, drive the fusion process, linking to a symbolic frame all the segmented regions characterized by similar properties. As a first application, road and obstacle detection is performed. A particular fusion strategy is tested that integrates results separately obtained by applying the recognition module to each different sensor according to the related model description. Preliminary results are very promising and confirm the validity of the proposed approach