An Efficient Parallel Algorithm for Multi-Scale Analysis of Connected Components in Gigapixel Images
| Autor: | Michael H. F. Wilkinson, Georgios K. Ouzounis, Martino Pesaresi |
|---|---|
| Přispěvatelé: | Intelligent Systems |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
differential attribute profile
Computer science Computation Geography Planning and Development 0211 other engineering and technologies Parallel algorithm SCALE-SPACE Concurrent algorithm 02 engineering and technology connected filters spatial signature CSL model image decomposition giga-pixel images Scale space Raster data Set (abstract data type) 0202 electrical engineering electronic engineering information engineering Earth and Planetary Sciences (miscellaneous) Segmentation Computer vision Computers in Earth Sciences GeneralLiterature_REFERENCE(e.g. dictionaries encyclopedias glossaries) 021101 geological & geomatics engineering RESOLUTION SATELLITE IMAGERY Connected component business.industry ComputingMethodologies_DOCUMENTANDTEXTPROCESSING 020201 artificial intelligence & image processing Artificial intelligence business Algorithm |
| Zdroj: | ISPRS International Journal of Geo-Information; Volume 5; Issue 3; Pages: 22 ISPRS International Journal of Geo-Information, 5(3). MDPI AG |
| ISSN: | 2220-9964 |
| Popis: | Differential Morphological Profiles (DMPs) and their generalized Differential Attribute Profiles (DAPs) are spatial signatures used in the classification of earth observation data. The Characteristic-Salience-Leveling (CSL) is a model allowing the compression and storage of the multi-scale information contained in the DMPs and DAPs into raster data layers, used for further analytic purposes. Computing DMPs or DAPs is often constrained by the size of the input data and scene complexity. Addressing very high resolution remote sensing gigascale images, this paper presents a new concurrent algorithm based on the Max-Tree structure that allows the efficient computation of CSL. The algorithm extends the “one-pass” method for computation of DAPs, and delivers an attribute zone segmentation of the underlying trees. The DAP vector field and the set of multi-scale characteristics are computed separately and in a similar fashion to concurrent attribute filters. Experiments on test images of 3.48 to 3.96 Gpixel showed an average computational speed of 59.85 Mpixel per second, or 3.59 Gpixel per minute on a single 2U rack server with 64 opteron cores. The new algorithms could be extended to morphological keypoint detectors capable of handling gigascale images. |
| Databáze: | OpenAIRE |
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