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Neural Networks for Interpretation of Remotely Sensed Data

Recent advances in sensor and computer technology are revolutionizing the way remotely sensed data is
collected, managed, and interpreted. The main purpose of this special issue is to provide a cross-section of the state-of-the-art in the area and to offer a thoughtful perspective on the potentials and the emerging challenges of applying ANNs to the analysis and interpretation of the new generation of remotely sensed data sets. The special issue will equally cover methodological innovations (e.g., development of new ANN architectures or modifications of existing ones, including advanced training strategies) and new applications of ANNs in EO and planetary exploration.

Edited by: Javier Plaza, Fabio Del Frate and Erzsebet Merenyi

  1. Content type: Research

    Efficient exploitation of hyperspectral imagery is of great importance in remote sensing. Artificial intelligence approaches have been receiving favorable reviews for classification of hyperspectral data becau...

    Authors: Erzsébet Merényi, William H Farrand, James V Taranik and Timothy B Minor

    Citation: EURASIP Journal on Advances in Signal Processing 2014 2014:71

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  2. Content type: Research

    The retrieval of the tropospheric ozone column from satellite data is very important for the characterization of tropospheric chemical and physical properties. However, the task of retrieving tropospheric ozon...

    Authors: Antonio Di Noia, Pasquale Sellitto, Fabio Del Frate, Marco Cervino, Marco Iarlori and Vincenzo Rizi

    Citation: EURASIP Journal on Advances in Signal Processing 2013 2013:21

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  3. Content type: Research

    This article presents a novel method for the enhancement of the spatial quality of hyperspectral (HS) images through the use of a high resolution panchromatic (PAN) image. Due to the high number of bands, the ...

    Authors: Giorgio Antonino Licciardi, Muhammad Murtaza Khan, Jocelyn Chanussot, Annick Montanvert, Laurent Condat and Christian Jutten

    Citation: EURASIP Journal on Advances in Signal Processing 2012 2012:207

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  4. Content type: Research

    Remote sensing images have been used productively for land cover identification to accurately manage and control agricultural and environmental resources. However, these images have often been interpreted inte...

    Authors: Kadim Taşdemir and Csaba Wirnhardt

    Citation: EURASIP Journal on Advances in Signal Processing 2012 2012:200

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  5. Content type: Research

    A novel system named unsupervised multiple classifier system (UMCS) for unsupervised classification of optical remote sensing data is presented. The system is based on integrating two or more individual classi...

    Authors: Ahmed AK Tahir

    Citation: EURASIP Journal on Advances in Signal Processing 2012 2012:165

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  6. Content type: Research

    In this article, a new technique for features extraction from SAR interferograms is presented. The technique combines the properties of auto-associative neural networks with those of more traditional approache...

    Authors: Matteo Picchiani, Fabio Del Frate, Giovanni Schiavon and Salvatore Stramondo

    Citation: EURASIP Journal on Advances in Signal Processing 2012 2012:155

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  7. Content type: Research

    Accurate knowledge of the spatial extents and distributions of an oil spill is very impor-tant for efficient response. This is because most petroleum products spread rapidly on the water surface when released ...

    Authors: Alireza Taravat and Fabio Del Frate

    Citation: EURASIP Journal on Advances in Signal Processing 2012 2012:107

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  8. Content type: Research

    This article presents a novel artificial neural network technique for merging multi-sensor satellite data. Stacked neural networks (NNs) are used to learn the temporal and spatial drifts between data from diff...

    Authors: Diego G Loyola and Melanie Coldewey-Egbers

    Citation: EURASIP Journal on Advances in Signal Processing 2012 2012:91

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