Multi-spectral Texture Characterisation for Remote Sensing Image Segmentation

Pla Filiberto, Gracia Gema, García-Sevilla Pedro, Majid Mirmehdi, Xie Xianghua

Research output: Contribution to journalArticle (Academic Journal)peer-review

8 Citations (Scopus)

Abstract

A multi-spectral texture characterisation model is proposed, the Multi-spectral Local Differences Texem – MLDT, as an affordable approach to be used in multi-spectral images that contain large number of bands. The MLDT is based on the Texem model. Using an inter-scale post-fusion strategy for image segmentation, framed in a multi-resolution approach, we produce unsupervised multi-spectral image segmentations. Preliminary results on several remote sensing multi-spectral images exhibit a promising performance by the MLDT approach, with further improvements possible to model more complex textures and add some other features, like invariance to spectral intensity.
Translated title of the contributionMulti-spectral Texture Characterisation for Remote Sensing Image Segmentation
Original languageEnglish
Pages (from-to)257-264
JournalProceedings of the 4th Iberian Pattern Recognition and Image Analysis Conference
Publication statusPublished - 2009

Bibliographical note

ISBN: 9783642021718
Publisher: Springer, Lecture Notes in Computer Science
Name and Venue of Conference: Proceedings of the 4th Iberian Pattern Recognition and Image Analysis Conference
Other identifier: 2000999

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