DMCA
Superresolution mapping for extraction of urban tree crown objects from VHR images.” The International Archives of the Photogrammetry, Remote Sensing and (2010)
| Venue: | In Press, Corrected Proof. "Classification and quantification of green in the |
| Citations: | 2 - 1 self |
BibTeX
@INPROCEEDINGS{Tolpekin10superresolutionmapping,
author = {Valentyn A. Tolpekin and Juan Pablo Ardila and Wietske Bijker},
title = {Superresolution mapping for extraction of urban tree crown objects from VHR images.” The International Archives of the Photogrammetry, Remote Sensing and},
booktitle = {In Press, Corrected Proof. "Classification and quantification of green in the},
year = {2010}
}
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Abstract
Extraction of individual tree crown objects in urban areas using available VHR space-borne imaging systems remains a challenging image processing task mainly because of two factors: the limited spectral information offered by the sensors and their limited spatial resolution. The spectral information of available VHR satellite sensors is not sufficient to discriminate tree crowns from other land cover classes such as grassland and shrubs using spectral pixel-based classification techniques. This can be solved by using a contextual classification approach, while the limitation of spatial resolution can be reduced by using Super resolution mapping (SRM). In this paper we extend the contextual Markov Random Field (MRF) based SRM method developed earlier for multispectral images to include the panchromatic band with higher spatial resolution. We apply this method for extraction of tree crown objects from Quickbird images, setting the spatial resolution of superresolved map to that of the panchromatic band, 0.6 m. We apply the proposed method for identification of tree crown objects in a residential area in the Netherlands. We find that incorporation of panchromatic band leads to improved object identification. Our object based accuracy assessment indicate that the proposed method identified 73 % of the trees in the study area, with some commission errors in tree areas with understory vegetation. 1
Keyphrases
urban tree crown object remote sensing spatial resolution panchromatic band superresolution mapping international archive vhr image tree crown object object identification understory vegetation challenging image multispectral image spectral information tree crown individual tree crown object srm method spectral pixel-based classification technique urban area accuracy assessment indicate land cover class available vhr tree area superresolved map study area limited spectral information residential area quickbird image contextual markov random field commission error super resolution mapping available vhr satellite sensor limited spatial resolution contextual classification approach
