Fabio Scotti - Manuel Roveri Universit à degli studi, Milano, Italy

1. Soft Computing for Environmental Applications and Remote SensingSoft computing for Remote Sensing Image Processing and Interpretation Fabio Scotti - Manuel Roveri Università degli studi, Milano, Italy

2. 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) Fabio Scotti - Manuel Roveri

3. 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. Fabio Scotti - Manuel Roveri

4. PART A Classical techniques Fabio Scotti - Manuel Roveri

5. 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 Fabio Scotti - Manuel Roveri

6. 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 Fabio Scotti - Manuel Roveri

7. PART B Soft-computing techniques Fabio Scotti - Manuel Roveri

8. 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 Fabio Scotti - Manuel Roveri

9. 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.pdflinked in the course page. Fabio Scotti - Manuel Roveri

10. 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.pdflinked in the course page. Fabio Scotti - Manuel Roveri

11. 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.pdflinked in the course page. Fabio Scotti - Manuel Roveri

12. 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.pdflinked in the course page. Fabio Scotti - Manuel Roveri

13. End of the lecture Fabio Scotti - Manuel Roveri