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  • Produktbild: Neurocomputation in Remote Sensing Data Analysis
  • Produktbild: Neurocomputation in Remote Sensing Data Analysis

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

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

22.12.2012

Herausgeber

Ioannis Kanellopoulos + weitere

Verlag

Springer Berlin

Seitenzahl

284

Maße (L/B/H)

23,5/15,5/1,7 cm

Gewicht

458 g

Auflage

Softcover reprint of the original 1st ed. 1997

Sprache

Englisch

ISBN

978-3-642-63828-2

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

22.12.2012

Herausgeber

Verlag

Springer Berlin

Seitenzahl

284

Maße (L/B/H)

23,5/15,5/1,7 cm

Gewicht

458 g

Auflage

Softcover reprint of the original 1st ed. 1997

Sprache

Englisch

ISBN

978-3-642-63828-2

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Neurocomputation in Remote Sensing Data Analysis
  • Produktbild: Neurocomputation in Remote Sensing Data Analysis
  • 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