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