Transcript Document

C17 SC for Environmental Applications and Remote Sensing
IMSCIA
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
UNIVERSITÀ DEGLI STUDI DI MILANO
C17 SC for Environmental Applications and Remote Sensing
IMSCIA
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
C17 SC for Environmental Applications and Remote Sensing
IMSCIA
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
C17 SC for Environmental Applications and Remote Sensing
IMSCIA
PART A
Classical techniques
Fabio Scotti - Manuel Roveri
C17 SC for Environmental Applications and Remote Sensing
IMSCIA
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:
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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.
Fabio Scotti - Manuel Roveri
(*) Goddard Space Flight Center, NASA
C17 SC for Environmental Applications and Remote Sensing
IMSCIA
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:
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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
C17 SC for Environmental Applications and Remote Sensing
PART B
Soft-computing
techniques
Fabio Scotti - Manuel Roveri
IMSCIA
C17 SC for Environmental Applications and Remote Sensing
IMSCIA
Towards advanced Remote Sensing
Image Processing and Automatic Interpretation (III)
• Let’s face the problem of interpretation/classification.
Our goals are now to understand:
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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.
Fabio Scotti - Manuel Roveri
(*) Goddard Space Flight Center, NASA
C17 SC for Environmental Applications and Remote Sensing
IMSCIA
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.
Fabio Scotti - Manuel Roveri
C17 SC for Environmental Applications and Remote Sensing
IMSCIA
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.
Fabio Scotti - Manuel Roveri
C17 SC for Environmental Applications and Remote Sensing
IMSCIA
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.
Fabio Scotti - Manuel Roveri
C17 SC for Environmental Applications and Remote Sensing
IMSCIA
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.
Fabio Scotti - Manuel Roveri
C17 SC for Environmental Applications and Remote Sensing
End of the lecture
Fabio Scotti - Manuel Roveri
IMSCIA