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Plenary Lecture

Computational Intelligence Techniques for Pattern Recognition from Remote Sensing Imagery

Professor Victor-Emil Neagoe
Polytechnic University of Bucharest
Romania
E-mail: victoremil@gmail.com

Abstract: Enormous successes have been achieved through modeling of biological and natural intelligence, resulting so-called „intelligent systems”. These nature-inspired intelligent technological paradigms are grouped under the umbrella called computational intelligence (CI). The main chapters of CI are: Artificial Neural Networks (ANN), Fuzzy Systems (FS), Evolutionary Computation (EC), Swarm Intelligence (SI), and Artificial Immune Systems (AIS). On the other side, modern environmental remote sensing imagery, owing to their large volume of high-resolution data, offer greater challenges for automated multispectral/hyperspectral image analysis. The algorithms are based on the fact that each class of materials, in accordance to its molecular composition, has its own spectral signature. Applications are needed both for remote sensing of urban/suburban infrastructure and socio-economic attributes and as well as to detect and monitor land-cover and land-use changes. Last years, several computational intelligence approaches have been used with promising degrees of success in remote sensing image analysis.
This lecture presents our approach dedicated to the improvement, experimentation and evaluation of several computational intelligence techniques for pattern recognition in remote sensing imagery. One considers three main directions and corresponding applications.
First section corresponds to neural networks for earth observation and it uses Concurrent Self-Organizing Maps, Multilayer Perceptron and Radial Basis Function neural networks in order to improve and evaluate multispectral and hyperspectral image classification performances. We have experimented these techniques both for LANDSAT 7ETM+ multispectral images and for several typical hyperspectral images (Indian Pines, Pavia University and Salinas). The neural networks for automated land-cover change detection have been also considered.
Second section is dedicated to Artificial Immune Systems (AIS) for supervised and unsupervised classification of multispectral images. These techniques are inspired from the vertebrate immune system, having strong capabilities of pattern recognition. We have implemented some improved AIS techniques for multispectral pixel classification from a LANDSAT 7ETM+ image.
Third section presents Ant Colony Optimization (ACO) model to develop and improve methods for feature selection and classification of remote sensing images. These techniques are inspired from the coordinated behavior of ant swarms. The considered ACO techniques are experimented for a Landsat 7ETM+ image dataset.

Brief Biography of the Speaker: Victor-Emil I. Neagoe was born in Pitesti (Arges county, Romania) on May 31, 1947. From 1965 till 1970 he attended the courses of the Faculty of Electronics and Telecommunications, Polytechnic Institute of Bucharest, Romania. In 1970 he received the M.S. degree of diplomat engineer in electronics and telecommunications as a head of his series (with Honor Diploma). He also obtained the Ph.D. degree in the same field from the same institution in 1976 as well as the Postgraduate Master degree in Applied Mathematics and Informatics from the Faculty of Mathematics, University of Bucharest in 1981. From 1970 till 1976 he has been an Assistant Professor at the Faculty of Electronics and Telecommunications, Polytechnic Institute of Bucharest, branches: Information Transmission Theory, Television, and Applied Electronics. From 1978 till 1991 he has been a Lecturer at the same Institute and Faculty, courses: Information Transmission Theory and Applied Electronics. Since 1991 he has been a Professor of the Polytechnic University of Bucharest, Romania, where he teaches the following courses: pattern recognition and artificial intelligence; digital signal processing; computational intelligence; data mining. He has been a Ph.D. supervisor since 1990. Prof. Neagoe has published more than 120 papers; his research interest includes pattern recognition, nature inspired intelligent techniques (computational intelligence), classification of multispectral and hyperspectral remote sensing imagery, emotion recognition from facial images, biometrics, sampling theory, image compression. He has been a Member of IEEE since 1978 and a Senior Member IEEE since 1984. Prof. Neagoe has been included in Who’s Who in the World and Europe 500 . Particularly, he has been recently included in Who’s Who in the World 2011 and 2012 (28th and 29th Editions) as well as in Who’s Who in Science and Engineering 2011-2012 (11th Edition).

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