Multi-spectral Texture Characterisation for Remote Sensing Image Segmentation

Authors:
Filiberto Pla;Gema Gracia;Pedro García-Sevilla;Majid Mirmehdi;Xianghua Xie
Affiliations:
Dept. Llenguatges i Sistemes Informàtics, University Jaume I, Castellón, Spain 12071;Dept. Llenguatges i Sistemes Informàtics, University Jaume I, Castellón, Spain 12071;Dept. Llenguatges i Sistemes Informàtics, University Jaume I, Castellón, Spain 12071;Dept. of Computer Science, University of Bristol, Bristol, UK BS8 1UB;Dept. of Computer Science, University of Swansea, Swansea, UK SA2 8PP
Venue:
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Year:
2009

Citing 4
Cited 0

Segmentation of Color Textures

IEEE Transactions on Pattern Analysis and Machine Intelligence
Epitomic analysis of appearance and shape

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
TEXEMS: Texture Exemplars for Defect Detection on Random Textured Surfaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Non parametric local density-based clustering for multimodal overlapping distributions

IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning

Quantified Score

Hi-index	0.00

Visualization

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 may 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.