Neurocomputation in Remote Sensing Data Analysis

Kanellopoulos, Ioannis|Wilkinson, Graeme G.|Roli, Fabio

ISBN 10: 3540633162 / ISBN 13: 9783540633167

Editore: Springer, Berlin, 1997

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A state-of-the-art view of recent developments in the use of artificial neural networks for analysing remotely sensed satellite data. Neural networks, as a new form of computational paradigm, appear well suited to many of the tasks involved in this image an. Codice articolo 908117821

Dati bibliografici

Titolo: Neurocomputation in Remote Sensing Data ...

Casa editrice: Springer, Berlin

Data di pubblicazione: 1997

Legatura: Gebunden

Condizione: New

Descrizione articolo

Riassunto:

This volume gives a state of the art view of recent developments in the use of artificial neural networks for the analysis of remotely sensed satellite data. Remote sensing has now become a discipline in which ever increasing volumes of data, gathered from space together with growing application needs for high precision spatial products, need to be interpreted in shorter times and with increasing accuracy. Neural networks, as a new form of computational paradigm, seem well suited to many of the tasks involved in remotely sensed image analysis. This book demonstrates a wide range of uses of neural networks for remote sensing applications and provides the views of a large number European experts brought together in the framework of a concerted action supported by the European Commission.

Contenuti:

Foreward - Introduction - Open Questions in Neurocomputing for Earth Observation - A Comparison of the Characterisation of Agricultural Land Using Singular Value Decomposition and Neural Networks - Land Cover Mapping from Remotely Sensed Data with a Neural Network: Accomodation Fuzziness - Geological Mapping Using Multi-Sensor Data: A Comparison of Methods - Application of Neural Networks and Order Statistics Filters to Speckle Noise Reduction in Remote Sensing Imaging - Neural Nets and Multichannel Image Processing Applications - Neural Networks for Classification of Ice Type Concentration from ERS-1 SAR Images. Classical Methods versus Neural Networks - A Neural Network Approach to Spectral Mixture Analysis - Comparison Between Systems of Image Interpretation - Feature Extraction for Neural Network Classifiers - Spectral Pattern Recognition by a Two-Layer Perceptron: Effects of Training Set Size - Comparison and Combination of Statistical and Neural Network Algorithms for Remote-Sensing Image Classification - Integrating the Alisa Classifier with Knowledge-Based Methods for Cadastral-Map Interpretation - A Hybrid Method for Preprocessing and Classification of SPOT Images - Testing some Connectionist Approaches for Thematic Mapping of Rural Areas - Using Artificial Recurrent Neural Nets to Identify Spectral and Spatial Patterns for Satellite Imagery Classification of Urban Areas - Dynamic Segmentation of Satellite Images Using Pulsed Coupled Neural Networks - Non-Linear Diffusion as a Neuron-Like Paradigm for Low-Level Vision - Application of the Constructive Mikado-Algorithm on Remotely Sensed Data - A Simple Neural Network Contextual Classifier - Optimising Neural Networks for Land Use Classification - High Speed Image Segmentation Using a Binary Neural Network - Efficient Processing and Analysis of Images Using Neural Networks - Selection of the Number of Clusters in Remote Sensing Images by Means of Neural Networks - A Comparative Study of Topological Feature Maps Versus Conventional Clustering for (Multi-Spectral) Scene. Identification in METEOSAT Imagery - Self Organised Maps: the Combined Utilisation of Feature and Novelty Detectors - Generalisation of Neural Network Based Segmentation. Results for Classification Purposes - Remote Sensing Applications which may be Addressed by Neural Networks Using Parallel Processing Technology - General Discussion