Abstract: Man has learned much from studies of natural systems, to develop new algorithmic models able to solve increasingly complex problems. 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).
On the other side, modern environmental remote sensing satellite imagery, owing to their large volume of high-resolution data, offer greater challenges for automated image analysis. The algorithms are based on the fact that each class of materials, in accordance to its molecularcomposition, has its own spectral signature.Applications are needed both for remote sensing of urban/suburban infrastructure and socio-economic attributes as well as to detect and monitor land-cover and land-use changes.Conventionally, pattern recognition in remote sensing imagery has been mainly based on classical statistical methods and decision theory. Last years, several computational intelligence approaches have been used with promising degrees of success in remote sensing image analysis.
This lecture is an approach dedicated to the improvement and experimentation of several nature-inspired intelligent models for pattern recognition in remote sensing imagery. One considers threemain models and corresponding applications.
First model is an Unsupervised Artificial Immune System (UAIS), inspired from the vertebrate immune system, having strong capabilities of pattern recognition. We have implemented this model for a LANDSAT 7ETM+ multispectral image from the region of Bucharest (Romania) with four pixel categories (agricultural fields, artificial surfaces, forest, and water) ; using UAIS, one leads to the correct clustering multispectral pixel score better than performances obtained by applying K-Means and Fuzzy K-Means algorithms.Second modeluses pixel classification byAnt Colony Optimization (ACO) algorithm which takes inspiration from the coordinated behavior of ant swarms. Using the ACO algorithm to remote sensing image classification does not assume an underlying statistical distribution for the pixel data, the contextual information can be taken into account, and it has strong robustness. The results of ACOclassification for a Landsat 7ETM+ image dataset(the same as that used in the first model) leads to very goodresults. Third modelcorresponds to change detection using neural network techniques:(a) Multilayer Perceptron (MLP); (b) Radial Basis Function Neural Network (RBF) ;( c) Supervised Self-Organizing Map (SOM). For comparison, one has tested change detection with statistical techniques (Bayes, Nearest Neighbor).The data used for the experiments of neural change detection are selections from a sequence of two LANDSAT 7 ETM+ multispectral images corresponding to the region Markaryd (Sweden),acquisitions from 2002 and 2006. Change detection by neural classifiers have led to better results than those obtained using statistical techniques.

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, average of marks 9.97 out of 10). He also obtained the Ph.D. degree in the same field from the same institution in 1976 (thesis supervisor Professor Gheorghe Cartianu) as well as the Postgraduate Master degree in Applied Mathematics and Informatics from the Faculty of Mathematics, University of Bucharest in 1981 (average of marks 10).
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; detection and estimation for information processing; digital signal processing; computational intelligence; data mining. He has been a Ph.D. supervisor since 1990; he co-ordinates now ten Ph.D. candidates. He has published more than 120 papers; his research interest includes pattern recognition, nature inspired intelligent techniques (computational intelligence), multispectral and hyperspectral satellite/aerial image analysis, image compression and recognition, biometrics, sampling theory.
He has been a Member of IEEE (Institute of Electrical and Electronics Eng., New York) 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 (28th Edition) as well as in Who’s Who in Science and Engineering 2011-2012 (11th Edition).