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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Multi-spectral Texture Characterisation for Remote Sensing Image Segmentation
verfasst von : Filiberto Pla, Gema Gracia, Pedro García-Sevilla, Majid Mirmehdi, Xianghua Xie
Erschienen in: Pattern Recognition and Image Analysis
Verlag: Springer Berlin Heidelberg
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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.