GLORIA

GEOMAR Library Ocean Research Information Access

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Online Resource
    Online Resource
    Copernicus GmbH ; 2020
    In:  The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Vol. XLIII-B2-2020 ( 2020-08-14), p. 1513-1519
    In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Copernicus GmbH, Vol. XLIII-B2-2020 ( 2020-08-14), p. 1513-1519
    Abstract: Abstract. Transfer learning methods reuse a deep learning model developed for a task on another task. Such methods have been remarkably successful in a wide range of image processing applications. Following the trend, few transfer learning based methods have been proposed for unsupervised multi-temporal image analysis and change detection (CD). Inspite of their success, the transfer learning based CD methods suffer from limited explainability. In this paper, we propose an explainable convolutional autoencoder model for CD. The model is trained in: 1) an unsupervised way using, as the bi-temporal images, patches extracted from the same geographic location; 2) a greedy fashion, one encoder and decoder layer pair at a time. A number of features relevant for CD is chosen from the encoder layer. To build an explainable model, only selected features from the encoder layer is retained and the rest is discarded. Following this, another encoder and decoder layer pair is added to the model in similar fashion until convergence. We further visualize the features to better interpret the learned features. We validated the proposed method on a Landsat-8 dataset obtained in Spain. Using a set of experiments, we demonstrate the explainability and effectiveness of the proposed model.
    Type of Medium: Online Resource
    ISSN: 2194-9034
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2020
    detail.hit.zdb_id: 2874092-0
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 2
    In: Ocean Science, Copernicus GmbH, Vol. 10, No. 3 ( 2014-05-06), p. 281-322
    Abstract: Abstract. This paper is the outcome of a workshop held in Rome in November 2011 on the occasion of the 25th anniversary of the POEM (Physical Oceanography of the Eastern Mediterranean) program. In the workshop discussions, a number of unresolved issues were identified for the physical and biogeochemical properties of the Mediterranean Sea as a whole, i.e., comprising the Western and Eastern sub-basins. Over the successive two years, the related ideas were discussed among the group of scientists who participated in the workshop and who have contributed to the writing of this paper. Three major topics were identified, each of them being the object of a section divided into a number of different sub-sections, each addressing a specific physical, chemical or biological issue: 1. Assessment of basin-wide physical/biochemical properties, of their variability and interactions. 2. Relative importance of external forcing functions (wind stress, heat/moisture fluxes, forcing through straits) vs. internal variability. 3. Shelf/deep sea interactions and exchanges of physical/biogeochemical properties and how they affect the sub-basin circulation and property distribution. Furthermore, a number of unresolved scientific/methodological issues were also identified and are reported in each sub-section after a short discussion of the present knowledge. They represent the collegial consensus of the scientists contributing to the paper. Naturally, the unresolved issues presented here constitute the choice of the authors and therefore they may not be exhaustive and/or complete. The overall goal is to stimulate a broader interdisciplinary discussion among the scientists of the Mediterranean oceanographic community, leading to enhanced collaborative efforts and exciting future discoveries.
    Type of Medium: Online Resource
    ISSN: 1812-0792
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2014
    detail.hit.zdb_id: 2183769-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 3
    Online Resource
    Online Resource
    Copernicus GmbH ; 2021
    In:  The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Vol. XLIII-B2-2021 ( 2021-06-28), p. 847-854
    In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Copernicus GmbH, Vol. XLIII-B2-2021 ( 2021-06-28), p. 847-854
    Abstract: Abstract. The paper presents DECAI - DEcay Classification using Artificial Intelligence, a novel study using machine learning algorithms to identify materials, degradations or surface gaps of an architectural artefact in a semi-automatic way. A customised software has been developed to allow the operator to choose which categories of materials to classify, and selecting sample data from an orthophoto of the artefact to train the machine learning algorithms. Thanks to Visual Programming Language algorithms, the classification results are directly imported into the H-BIM environment and used to enrich the H-BIM model of the artefact. To date, the developed tool is dedicated to research use only; future developments will improve the graphical interface to make this tool accessible to a wider public.
    Type of Medium: Online Resource
    ISSN: 2194-9034
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2021
    detail.hit.zdb_id: 2874092-0
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...