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2015
Conference Paper
Titel
Fast segmentation of hyperspectral images by combining textural and spectral information
Abstract
In this paper different approaches for dimensionality reduction of hyperspectral images are compared in order to apply a texture based segmentation algorithm which efficiently operates in low dimensions. Detection of Powdery Mildew infection is used as a real-world test case for the proposed approach. For this application, an adaptive Principal Component Analysis based projection preserves the most relevant spectral information within the combined process of spatial-spectral feature extraction.