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Krupiński M., Drzewiecki W., Wawrzaszek A., Aleksandrowicz S. Initial Evaluation of the Applicability of Multifractal Measures as Global Content-Based Image Descriptors. ESA-EUSC-JRC 8th conference on image information mining; 10/2013
Increasing amount of Very High Resolution (VHR) data requires new methods of information mining. In this paper we describe applicability of multifractal theory for VHR panchromatic image analysis. The aim of the study was
Drzewiecki W., Wawrzaszek A., Aleksandrowicz S., Krupinski M., Bernat K. Comparison of selected textural features as global content-based descriptors of VHR satellite image. Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
The paper presents the results of the study of usefulness of selected textural features for describing the content of image chips (1024 by 1024 pixels) cut from WorldView-2 panchromatic images. Several texture analysis techniques
Wawrzaszek A., Krupinski, M. ; Aleksandrowicz S., Drzewiecki W. Fractal and multifractal characteristics of very high resolution satellite images. Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International.
In our work we analyse fractal and multifractal characteristics for description and extraction of information from very high spatial resolution satellite images. In particular, we propose the degree of multifractality as a parameter for
Wawrzaszek A., Krupiński M., Aleksandrowicz S., Drzewiecki W.: Formalizm multifraktalny w analizie zobrazowań satelitarnych. Archiwum Fotogrametrii, Kartografii i Teledetekcji, vol. 25, s.261–272, 2013
W pracy przedstawiamy formalizm multifraktalny, jako narzędzie wspomagające opis i ekstrakcję informacji z wysokorozdzielczych zobrazowań satelitarnych. Podejście to opiera się na założeniu, że na pojedynczy obraz (multifraktal) składa się wiele fraktali, każdy o innym
Drzewiecki W., Wawrzaszek A., Krupiński M., Aleksandrowicz S., Bernat K.: Comparison of selected textural features as global content-based descriptors of VHR satellite image – the EROS-A study. 2013 Federated Conference on Computer Science and Information
Texture is considered as one of the most crucial image features used commonly in computer vision. It is important source of information about image content, especially for single-band images. In this paper we present