Neurocomputation in Remote Sensing Data Analysis: Proceedings of Concerted Action Compares (Connectionist Methods for Pre-Processing and Analysis of Remote Sensing Data)

Kanellopoulos, I. (Editor)/ Wilkinson, G. G. (Editor)/ Roli, F. (Editor)/ Austin, J. (Editor)

ISBN 10: 3540633162 / ISBN 13: 9783540633167

Edité par Springer Nature, 1997

A propos de cet article

284 pages. 9.75x6.50x1.00 inches. In Stock.

N° de réf. du vendeur x-3540633162

Détails bibliographiques

• Titre: Neurocomputation in Remote Sensing Data Analysis: Proceedings of Concerted Action Compares (Connectionist Methods for Pre-Processing and Analysis of Remote Sensing Data)
• Éditeur: Springer Nature
• Date de parution: 1997
• Langue: anglais
• ISBN à 10 chiffres: 3540633162
• ISBN à 13 chiffres: 9783540633167
• Reliure: Hardcover
• État de l'article: Brand New

A propos de ce titre

Synopsis

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