Neurocomputation in Remote Sensing Data Analysis (eBook, PDF)

Proceedings of Concerted Action COMPARES (Connectionist Methods for Pre-Processing and Analysis of Remote Sensing Data)
Redaktion: Kanellopoulos, Ioannis; Austin, James; Roli, Fabio; Wilkinson, Graeme G.

• Format: PDF

Produktbeschreibung

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 analysis. This book demonstrates a wide range of uses of neural networks for remote sensing applications and reports the views of a large number of European experts brought together as part of a concerted action supported by the European Commission.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 284
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9783642590412
• Artikelnr.: 53108216

Inhaltsangabe

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: Accommodating 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.- Seif 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.