Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have now become the standard approach to many remote sensing applications. With algorithms that combine statistics and geometry, kernel methods have proven successfulΒ
Neurocomputation in Remote Sensing Data Analysis: Proceedings of Concerted Action COMPARES (Connectionist Methods for Pre-Processing and Analysis of Remote Sensing Data)
β Scribed by Ioannis Kanellopoulos (auth.), Ioannis Kanellopoulos, Prof. Graeme G. Wilkinson, Dr. Fabio Roli, Dr. James Austin (eds.)
- Publisher
- Springer-Verlag Berlin Heidelberg
- Year
- 1997
- Tongue
- English
- Leaves
- 291
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
Since 1994 the European Commission has been supporting activities under the Environment and Climate programme of research and technological deΒ velopment, with the aim of developing cost-effective applications of satellite Earth observation (EO) for both environmental monitoring and research. This action has included support to methodological research, aimed at the development and evaluation of new 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 10 years, together with the growing interest ofthe Earth observation community in this approach as a tool for data interpretation, the Commission decided in 1995 to support the concerted action COMPARES, following a proposal from a group of acknowledged European experts.
β¦ Table of Contents
Front Matter....Pages i-ix
Introduction....Pages 1-2
Open Questions in Neurocomputing for Earth Observation....Pages 3-13
A Comparison of the Characterisation of Agricultural Land Using Singular Value Decomposition and Neural Networks....Pages 14-27
Land Cover Mapping from Remotely Sensed Data with a Neural Network: Accommodating Fuzziness....Pages 28-37
Geological Mapping Using Multi-Sensor Data: A Comparison of Methods....Pages 38-46
Application of Neural Networks and Order Statistics Filters to Speckle Noise Reduction in Remote Sensing Imaging....Pages 47-56
Neural Nets and Multichannel Image Processing Applications....Pages 57-70
Neural Networks for Classification of Ice Type Concentration from ERS-1 SAR Images....Pages 71-78
A Neural Network Approach to Spectral Mixture Analysis....Pages 79-85
Comparison Between Systems of Image Interpretation....Pages 86-96
Feature Extraction for Neural Network Classifiers....Pages 97-104
Spectral Pattern Recognition by a Two-Layer Perceptron: Effects of Training Set Size....Pages 105-116
Comparison and Combination of Statistical and Neural Network Algorithms for Remote-Sensing Image Classification....Pages 117-124
Integrating the Alisa Classifier with Knowledge-Based Methods for Cadastral-Map Interpretation....Pages 125-133
A Hybrid Method for Preprocessing and Classification of SPOT Images....Pages 134-141
Testing some Connectionist Approaches for Thematic Mapping of Rural Areas....Pages 142-150
Using Artificial Recurrent Neural Nets to Identify Spectral and Spatial Patterns for Satellite Imagery Classification of Urban Areas....Pages 151-159
Dynamic Segmentation of Satellite Images Using Pulsed Coupled Neural Networks....Pages 160-167
Non-Linear Diffusion as a Neuron-Like Paradigm for Low-Level Vision....Pages 168-175
Application of the Constructive Mikado-Algorithm on Remotely Sensed Data....Pages 176-185
A Simple Neural Network Contextual Classifier....Pages 186-193
Optimising Neural Networks for Land Use Classification....Pages 194-201
High Speed Image Segmentation Using a Binary Neural Network....Pages 202-213
Efficient Processing and Analysis of Images Using Neural Networks....Pages 214-223
Selection of the Number of Clusters in Remote Sensing Images by Means of Neural Networks....Pages 224-231
A Comparative Study of Topological Feature Maps Versus Conventional Clustering for (Multi-Spectral) Scene Identification in METEOSAT Imagery....Pages 232-241
Self Organised Maps: the Combined Utilisation of Feature and Novelty Detectors....Pages 242-254
Generalisation of Neural Network Based Segmentation Results for Classification Purposes....Pages 255-261
Remote Sensing Applications Which may be Addressed by Neural Networks Using Parallel Processing Technology....Pages 262-279
General Discussion....Pages 281-284
β¦ Subjects
Artificial Intelligence (incl. Robotics); Economic Geology; Regional/Spatial Science; Pattern Recognition; Geography (general); Earth Sciences, general
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