Paper
24 February 2004 Kernel-based reclassification algorithm applied on very high spatial resolution satellite imagery of complex ecosystems
Iphigenia Keramitsoglou, Charalambos Kontoes, Panagiotis Elias, Nicolaos Sifakis, Eleni Fitoka, Stefan Weiers
Author Affiliations +
Abstract
Kernel-based reclassification algorithm derives information on specific thematic classes on the basis of the frequency and spatial arrangement of land cover classes within a square kernel. This algorithm has been originally developed and validated for the urban environment. The present work investigates the potential of projecting this technique to the classification of very high spatial resolution satellite imagery of natural ecosystems. For that purpose a software tool has been developed. The output, apart from the reclassified image, includes a post-classification probability map which shows the areas where the kernel reclassification algorithm has given valid results. The software was tested on an IKONOS image of Lake Kerkini (Greece), a wetland of great ecological value, included in the NATURA 2000 list of ecosystems. The results show that the algorithm has responded successfully in most cases overcoming problems previously encountered by pixel-based classifiers, such as pixel noise.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Iphigenia Keramitsoglou, Charalambos Kontoes, Panagiotis Elias, Nicolaos Sifakis, Eleni Fitoka, and Stefan Weiers "Kernel-based reclassification algorithm applied on very high spatial resolution satellite imagery of complex ecosystems", Proc. SPIE 5232, Remote Sensing for Agriculture, Ecosystems, and Hydrology V, (24 February 2004); https://doi.org/10.1117/12.511071
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Cited by 2 scholarly publications.
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KEYWORDS
Earth observing sensors

Satellites

Ecosystems

High resolution satellite images

Spatial resolution

Image classification

Algorithm development

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