Abstract
Building highly discriminative graph has an important impact on the quality of graph-based hyperspectral image segmentation. For this purpose, we propose to weight graph edges using Local Binary Pattern (LBP) descriptor that takes into account the texture information of the hyperspectral images. Nodes in the graph embed spectral LBP features computed from the different hyperspectral bands, while edges encode the spatial relationship between these features.
The multiphase level set method is then applied on the constructed graph to segment the image. We validate the proposed method, using Overlapping Score evaluation metric, on several popular hyperspectral images. The results show that our method is very efficient compared to other state-of-the-art one.
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Tabia, K., Desquesnes, X., Lucas, Y., Treuillet, S. (2019). Local Binary Pattern Based Graph Construction for Hyperspectral Image Segmentation. In: Conte, D., Ramel, JY., Foggia, P. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2019. Lecture Notes in Computer Science(), vol 11510. Springer, Cham. https://doi.org/10.1007/978-3-030-20081-7_15
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DOI: https://doi.org/10.1007/978-3-030-20081-7_15
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