On the optimization and selection of wavelet texture for feature extraction from high‐resolution satellite imagery with application towards urban‐tree delineation.
In: International Journal of Remote Sensing, Jg. 27 (2006-01-10), Heft 1/2, S. 73-104
Online
academicJournal
Zugriff:
Integration of spectral and multi-scale texture is proposed in order to improve the detection and classification of urban-trees from QuickBird imagery. Arguing that spatial-structure semantic information exits at a hierarchy of scales and that texture is a consequence of objects in the hierarchy, multi-scale wavelets decomposition is proposed for the extraction of vertical, horizontal and diagonal texture components. Pre-selection of texture sub-bands is achieved via mean, entropy, variance and second angular moment. The resulting sub-bands are analysed for separability between trees and similarly reflecting features, such as rice-paddy, grass/lawns, open ground and playground, based on Kullback–Leibler (KL) divergence and Battacharyya distance. The results are ranked and classified with k -means. In comparison with the field data, KL gave the best results with omission and commission error of 4.4%. The proposed methodology has the ability to capture the increased natural variability in reflectance and improved the accuracy by 23.6%, in comparison with spectral-only. [ABSTRACT FROM AUTHOR]
Titel: |
On the optimization and selection of wavelet texture for feature extraction from high‐resolution satellite imagery with application towards urban‐tree delineation.
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Autor/in / Beteiligte Person: | Ouma, YashonO. ; Ngigi, T. G. ; Tateishi, R. |
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Zeitschrift: | International Journal of Remote Sensing, Jg. 27 (2006-01-10), Heft 1/2, S. 73-104 |
Veröffentlichung: | 2006 |
Medientyp: | academicJournal |
ISSN: | 0143-1161 (print) |
DOI: | 10.1080/01431160500295885 |
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