U N I V E R S I T À D E G L I S T U D I D I M I L A N O C17 SC for Environmental Applications and Remote Sensing I M S C I A Soft Computing for Environmental Applications and Remote Sensing Soft computing for Remote Sensing Image Processing and Interpretation Fabio Scotti - Manuel Roveri Università degli studi, Milano, Italy

C17 SC for Environmental Applications and Remote Sensing I M S C I A Fabio Scotti - Manuel Roveri Introduction (I) In order to take advantage and make good use of remote sensing data, we must be able to extract meaningful information from the imagery. Interpretation and analysis of remote sensing imagery involves the identification and/or measurement of various targets in an image in order to extract useful information about them. Soft computing methods can be used in many applications and in many modules of a remote sensing systems (i.e., the design of the system, preprocessing modules, enhancement modules, classification modules)

C17 SC for Environmental Applications and Remote Sensing I M S C I A Fabio Scotti - Manuel Roveri Introduction (II) In this lecture we firstly introduce the basics in image processing, in particular the following techniques: –Preprocessing; –Enhancement; –automatic Classification and Interpretation. In the second part of the lesson we will present the main soft-computing techniques used in Remote Sensing and in the environmental applications.

C17 SC for Environmental Applications and Remote Sensing I M S C I A Fabio Scotti - Manuel Roveri PART A Classical techniques

C17 SC for Environmental Applications and Remote Sensing I M S C I A Fabio Scotti - Manuel Roveri The basics of the Remote Sensing Image Processing and Automatic Interpretation (I) Our goal is to understand the basic techniques to analyze the RS images, in particular: –Element of visual interpretation; –Basic of Digital Image Processing; –Preprocessing; –Image enhancement; –Image Transformations; –Image Classification, Analysis and Data Integration; Please read carefully the tutorial L3_Analysis1.pdf (*) linked in the course page. (*) Goddard Space Flight Center, NASA

C17 SC for Environmental Applications and Remote Sensing I M S C I A Fabio Scotti - Manuel Roveri The basics of the Remote Sensing Image Processing and Automatic Interpretation (II) Our goals are to understand the first techniques to extract information from RS image, focalizing on an applicative example. Important issues are: –Band Information Characteristics; –False Color View; –True Color View; –Contrast Stretching and Spatial Filtering; –Principal Components Analysis; –Image Ratioing; Please read carefully the tutorial L3_Analysis2.pdf (*) linked in the course page. (*) Goddard Space Flight Center, NASA

C17 SC for Environmental Applications and Remote Sensing I M S C I A Fabio Scotti - Manuel Roveri PART B Soft-computing techniques

C17 SC for Environmental Applications and Remote Sensing I M S C I A Fabio Scotti - Manuel Roveri Towards advanced Remote Sensing Image Processing and Automatic Interpretation (III) Let’s face the problem of interpretation/classification. Our goals are now to understand: –Unsupervised Classification; –Supervised Classification; –Minimum Distance Classification; –Maximum Likelihood Classification; –Application of a Probabilistic Neural Network Classifier. Please read carefully the tutorial L3_Analysis3.pdf (*) linked in the course page. (*) Goddard Space Flight Center, NASA

C17 SC for Environmental Applications and Remote Sensing I M S C I A Fabio Scotti - Manuel Roveri An overview of Soft Computing methods for Spectral Image Analysis Exploitation of the wealth of information in spectral images has yet to match up to the sensors' capabilities, as conventional methods often prove inadequate. ANNs hold the promise to revolutionize this area by overcoming many of the mathematical obstacles that traditional techniques fail at. By providing high speed when implemented in parallel hardware, (near-)real time processing of extremely high data volumes, typical in remote sensing spectral imaging, will also be possible. Please read the paper L3_Paper1.pdf linked in the course page.

C17 SC for Environmental Applications and Remote Sensing I M S C I A Fabio Scotti - Manuel Roveri Knowledge discovery from multispectral Satellite Images by Fuzzy Neural Networks Fuzzy Neural Networks can provide approaches to extract knowledge from multispectral images. For example it is possible to optimize classification rules using fuzzy neural networks. The goal of the reading is to understand how the knowledge can be transferred and exploited into the Fuzzy-NN with respect to this application. Please read the paper L3_Paper2.pdf linked in the course page.

C17 SC for Environmental Applications and Remote Sensing I M S C I A Fabio Scotti - Manuel Roveri A temporally neural adaptive classifier for multispectral imagery In this work we can see how a probabilistic neural network (PNN) is devised to account for the changes in the feature space as a result of environmental variations. The proposed methodology is used to develop a pixel-based cloud classification system. Please read the paper L3_Paper3.pdf linked in the course page.

C17 SC for Environmental Applications and Remote Sensing I M S C I A Fabio Scotti - Manuel Roveri Satellite constellation design using genetic algorithm The automatic satellite constellation design with satellite diversity and radio resource management is a problem that can be successfully solved using genetic algorithms methods. The automatic satellite constellation design means that some parameters of satellite constellation design can be determined simultaneously. The total number of satellites, the altitude of a satellite, the angle between planes, the angle shift between satellites and the inclination angle are considered in the design. Satellite constellation design can modeled using a multiobjective genetic algorithm. Please read the paper L3_Paper4.pdf linked in the course page.

C17 SC for Environmental Applications and Remote Sensing I M S C I A Fabio Scotti - Manuel Roveri End of the lecture