Ioannis Kanellopoulos - Böcker
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3 produkter
3 produkter
Neurocomputation in Remote Sensing Data Analysis
Proceedings of Concerted Actions "Compares" (Connectionist Methods for Pre-Processing and Analysis of Remote Sensing Data)
Inbunden, Engelska, 1997
856 kr
Skickas inom 10-15 vardagar
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.
Neurocomputation in Remote Sensing Data Analysis
Proceedings of Concerted Action COMPARES (Connectionist Methods for Pre-Processing and Analysis of Remote Sensing Data)
Häftad, Engelska, 2012
538 kr
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Since 1994 the European Commission has been supporting activities under the Environment and Climate programme of research and technological de 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.
Machine Vision and Advanced Image Processing in Remote Sensing
Proceedings of Concerted Action MAVIRIC (Machine Vision in Remotely Sensed Image Comprehension)
Häftad, Engelska, 2011
1 069 kr
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Since 1994, the European Commission has undertaken various actions to expand the use of Earth observation (EO) from space in the Union and to stimulate value-added services based on the use of Earth observation satellite data.' By supporting research and technological development activities in this area, DG XII responded to the need to increase the cost-effectiveness of space derived environmental information. At the same time, it has contributed to a better exploitation of this unique technology, which is a key source of data for environmental monitoring from local to global scale. MAVIRIC is part of the investment made in the context of the Environ ment and Climate Programme (1994-1998) to strengthen applied techniques, based on a better understanding of the link between the remote sensing signal and the underlying bio- geo-physical processes. Translation of this scientific know-how into practical algorithms or methods is a priority in order to con vert more quickly, effectively and accurately space signals into geographical information. Now the availability of high spatial resolution satellite data is rapidly evolving and the fusion of data from different sensors including radar sensors is progressing well, the question arises whether existing machine vision approaches could be advantageously used by the remote sensing community. Automatic feature/object extraction from remotely sensed images looks very attractive in terms of processing time, standardisation and implementation of operational processing chains, but it remains highly complex when applied to natural scenes.