Table Of ContentNeurocomputation
in Remote Sensing
Data Analysis
Springer-Verlag Berlin Heidelberg GmbH
I. Kanellopoulos · G. G. Wilkinson
F. Roli · J. Austin (Eds.)
Neurocomputation
in Remote Sensing
Data Analysis
Proceedings of Concerted Action COMPARES
(Connectionist Methods for Pre-Processing
and Analysis of Remote Sensing Data)
With 87 Figures
and 39 Tables
, Springer
loannis Kanellopoulos
Joint Research Centre, Commission of the European Communities
Space Applications Institute
Environmental Mapping and Modelling Unit
1-21020 Ispra (Varese), Italy
Prof. Graeme G. Wilkinson
Kingston University
Head, School of Computer Science and Electronic Systems
Penrhyn Road
Kingston Upon Thames, KT1 2EE, United Kingdom
Dr. Fabio Roli
University of Cagliari
Electrical and Electronic Engineering Department
Piazza d'A rmi
1-09123 Cagliari, Italy
Dr. James Austin
University of York, Department of Computer Science
Advanced Computer Architecture Group
York, YOI SDD, United Kingdom
ISBN 978-3-642-63828-2
Cataloging-in-Publicat ion Data applied for
Die Deutsche Bibliothek -C1P-Einheitsaufnahme
Neurocomputation in remote sensing data analysis: proceedings of concerted
action COMPARES ( connectionist methods for pre-processing and analysis of
remote sensing data) ; with 39 tables II. Kanellopoulos ... (ed.). -Berlin;
Heidelberg; New York; Barcelona; BlIdapest ; Hong Kong; London ; Milan ;
Paris; Santa Clara; Singapore; Tokyo: Springer. 1997
ISBN 978-3-642-63828-2 ISBN 978-3-642-59041-2 (eBook)
DOI 10.1007/978-3-642-59041-2
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Foreword
Since 1994 the European Commission has been supporting activities under
the Environment and Climate programme of research and technological de
velopment, with the aim of developing cost-effective applications ofsatellite
Earth observation (EO) for both environmental monitoring and research.
This action has included support to methodological research, aimed at
the development and evaluation ofnew techniques forming part ofthe chain
of processing needed to transform data into useful information. Wherever
appropriate, the Commission has emphasised the coordination of ongoing
research funded at the national level, through the mechanism of concerted
actions. Concerted actions are flexible and efficient means to marshal efforts
at the European level for a certain period. They are proposed by groups of
researchers active in a given field who have identified the added value to
be gained by European cooperation, whilst continuing to pursue their own
individual projects.
In view of the rapid developments in the field of neural network over
the last 10years, together with the growinginterest ofthe Earth observation
communityin thisapproachasatoolfor datainterpretation, the Commission
decided in 1995 to support the concerted action COMPARES, following a
proposal from a group ofacknowledged European experts.
The COMPARES concerted action provided a forum for a thorough re
viewofthestate-of-the-art,and a Europe-widedebateonfuture researchpri
orities. The present volume, arising from a highly successful workshop held
in July 1996, will provide a valuable reference for those working in this field,
and will help indicatethe most promisingapplications ofneural networks for
further development and eventual operational use.
Alan Cross
Environment & Climate Programme
DG XII: Science, research and development
European Commission
Table of Contents
Foreword..................................................... V
Introduction. ................................................. 1
Open Questions in Neurocomputing for Earth Observation
Graeme G. Wilkinson 3
A Comparison ofthe Characterisation ofAgricultural Land
Using Singular Value Decomposition and Neural Networks
S. Danaher, G. Herries, T. Selige, and M. Mac Suirtan. .............. 14
Land Cover Mapping from Remotely Sensed Data with a
Neural Network: Accommodating Fuzziness
Giles M. Foody ................................................. 28
Geological Mapping Using Multi-Sensor Data: A
Comparison ofMethods
Paul M. Mather, Brandt Tso, and Magaly Koch 38
Application ofNeural Networks and Order Statistics Filters
to Speckle Noise Reduction in Remote Sensing Imaging
E. Kofidis, S. Theodoridis, C. Kotropoulos, and I. Pitas , 47
Neural Nets and Multichannel Image Processing
Applications
Vassilis Gaganis, Michael Zervakis, and Manolis Christodoulou. ....... 57
Neural Networks for Classification ofIce Type
Concentration from ERS-l SAR Images. Classical Methods
versus Neural Networks
Jan Depenau .................................... ............... 71
A Neural Network Approach to Spectral Mixture Analysis
Theo E. Schouten and Maurice S. klein Gebbinck ................... 79
Comparison Between Systems ofImage Interpretation
Jean-Pierre Cocquerez, Sylvie Philipp, and Philippe Gaussier ......... 86
viii Table ofContents
Feature Extraction for Neural Network Classifiers
Jon A. Benediktsson and Johannes R. Sveinsson .................... 97
Spectral Pattern Recognition by a Two-Layer Perceptron:
Effects ofTraining Set Size
Petra Staufer and Manfred M. Fischer 105
Comparison and Combination ofStatistical and Neural
Network Algorithms for Remote-Sensing Image
Classification
Fabio Roli, Giorgio Giacinto, and Gianni Vernazza 117
Integrating the Alisa Classifier with Knowledge-Based
Methods for Cadastral-Map Interpretation
Eleni Stroulia and Rudolf Kober 125
A Hybrid Method for Preprocessing and Classification of
SPOT Images
Shan Yu and Konrad Weigl 134
Testing some Connectionist Approaches for Thematic
Mapping ofRural Areas
Leopoldo Cortez, Fernando Dura-o, and Vitorino Ramos 142
Using Artificial Recurrent Neural Nets to Identify Spectral
and Spatial Patterns for Satellite Imagery Classification of
Urban Areas
SaraSilva and Mario Caetano 151
Dynamic Segmentation of Satellite Images Using Pulsed
Coupled Neural Networks
X. Clastres, M. Samuelides, and G. L. Tan 160
Non-Linear Diffusion as a Neuron-Like Paradigm for
Low-Level Vision
M. Proesmans, L. J. Van Gool, and P. Vanroose '" 168
Application ofthe Constructive Mikado-Algorithm on
Remotely Sensed Data
C. Cruse, S. Leppelmann, A. Burwick, and M. Bode 176
A Simple Neural Network Contextual Classifier
Jens Tidemann and Allan Aasbjerg Nielsen 186
Optimising Neural Networks for Land Use Classification
Horst Bischofand Ales Leonardis 194
Table ofContents
IX
High Speed Image Segmentation Using a Binary Neural
Network.
Jim Austin 202
Efficient Processing and Analysis ofImages Using Neural
Networks
Stefanos Kollias 214
Selection ofthe Number of Clusters in Remote Sensing
Images by Means ofNeural Networks
Paolo Gamba, Andrea Marazzi, and Alessandro Mecocci 224
A Comparative Study ofTopological Feature Maps Versus
Conventional Clustering for (Multi-Spectral) Scene
Identification in METEOSAT Imagery
P. Boekaerts, E. Nyssen, and J. Camelis 232
SelfOrganised Maps: the Combined Utilisation ofFeature
and Novelty Detectors
C. N. Stefanidis and A. P. Cracknell 242
Generalisation of Neural Network Based Segmentation
Results for Classification Purposes
Ari Visa and Markus Peura 255
Remote Sensing Applications Which may be Addressed by
Neural Networks Using Parallel Processing Technology
Charles Day. ................................................... 262
General Discussion
Graeme G. Wilkinson 281
Introduction
Neural network or connectionist algorithms have made an enormous impact
in the field of signal processing over the last decade. Although few would
regard them as the perfect answer to all pattern recognition problems, there
is little doubt that they have contributed significantly to the solution of
some of the most difficult ones. Trainable networks of primitive processing
elements have been shownto becapableofdescribingand modellingsystems
ofgreatcomplexitywithoutthenecessityofbuildingparameterisedstatistical
descriptions. Such capability has led to considerable and constantly growing
interestfromtheremotesensingcommunity.Fromearlybeginningsinthelate
1980's, neural network algorithms are now being explored for a wide range
ofuses in Earth observation. In many cases these uses are still experimental
or at the pre-operational stage.
Thereisstilla needtoshareexperiencesandtoassessthebestapproaches
to solve particular kinds of pattern recognition problems in remote sensing.
Moreover with each new generation ofsatellites, the complexity and capture
rateofremotelysenseddatagrowconsiderablymakingitessentialtodevelop
new product extraction procedures which are powerful, yet based on simple
mathematical approaches, and are also suitablefor high speed distributed or
parallelforms ofcomputerarchitectures. Itis nosurprisethereforethat much
attentionhasrecentlyfocused onneuralnetworkorconnectionistapproaches.
This volume contains chapters based on presentations made at the York
workshop which was held as partofthe oneyear European ConcertedAction
"COMPARES" (COnnectionist Methods for Pre-processing and Analysis of
REmote Sensing data) during July 1996. Theobjective ofthis workshop was
to bring together key researchers -primarily but not exclusively from within
the European Union- toexposeand debatesomeofthe potential connection
ist approaches which can be used in remote sensing and to highlight issues
and topics for future research. Not unsurprisingly, it became apparent both
from the presentations made in York and from the chapters prepared for
this volume that a very wide range of different neural network models are
now being explored in remote sensing and that they are being used for a
considerable range of applications. During the workshop, the sessions were
focusedonparticularthemessuchastheapplicationsofmultilayerneuralnet
works, data selection and representation, hybrid methods for data analysis,
alternative neural network methods, performance, design and implementa
tion issues, the applications of neural data clustering methods, and finally
future prospects. In general there emerged a significant amount ofoptimism
at the York meeting, and an appreciation that collectively the application of
connectionist systems in remote sensing is moving forward at a fast pace, in
I. Kanellopoulos et al. (eds.), Neurocomputation in Remote Sensing Data Analysis
© Springer-Verlag Berlin · Heidelberg 1997
2 Introduction
Europe, with many interesting results. We believe this optimism is reflected
in the individual chapters within this present volume.
Whilst it would not be appropriate to attempt to summarise the whole
book here, one key finding that emerged from the York workshop was that
although multispectral image classification has dominated so far as the pri
mary application ofneural networks in remote sensing, alternative uses such
as signal inversion appear to be equally important. In the classification con
text, neural networks appear to gain over more traditional statistical meth
ods becausethey do not require the dataset to conform to a fixed statistical
model. This aspect becomes very powerful in the context of fusion of mul
tiple and independently gathered data sets -an increasing requirement in
Earth observation. Also in the signal inversion area, neural networks appear
to gain over more traditional approaches, but in this case because they can
be used to describe a non-linear mapping from one data space to another.
This non-linearity aspect also appears to be one of the main strengthsof
some connectionist approaches. Although the York workshop provided a fo
rum for the exchange ofideas and views on many such issues, the audience
was necessarily limited in number and one of the aims of this book is to
bring some ofthe topics to a wider audience in the hope that it will lead to
an acceleration ofthe pace ofdiscovery in the field ofconnectionism applied
to Earth observation. There is much work still to be done, though we can
confidently say that thefoundations ofa whole new generationofalgorithms
for the analysis ofremote sensing data have already been laid. It remains to
be seen if the connectionist approaches described herein really begin to take
over as the basis ofmost core analytical procedures in Earth observation in
the years to come.
The editors would like to acknowledge the high quality of presentations
madeattheYorkworkshopandtothanktheauthorsofthechaptersincluded
here, for their cooperation in making what we hope will be an important
contribution to the science of remote sensing. We would also like to thank
Ken Lees in the Department of Computer Science at the University ofYork
for the excellent organisation of the scientific and social events during the
workshop. We are also grateful to Charles Day and Susan Harding for their
assistance in the editing ofthe proceedings.
Finally we would like to express our gratitude to the Environment and
Climate research and development programme ofthe European Commission
for supporting the COMPARES initiative (contract no. ERB ENV4-CT95
0151).
Ioannis Kanellopoulos J.R.C. Ispra
Graeme G. Wilkinson February 1997
Fabio Roli
James Austin
