Hyperspectral imaging provides more information than conventional RGB images. However, its high dimensionality prevents its adaptation to the existing image processing techniques. Defining full-band spectral feature is the first missing step, which is currently dealt with indirectly by band selection or dimension reduction. This article proposes a spectral feature extraction method using the mathematical moments to quantify the shape of the reflectance spectrum from different aspects. A whole family of features is presented by changing the moment attributes. All the features and their combinations are extensively tested in texture analysis of a new hyperspectral image database from textile samples (SpecTex). Two supervised experiments are performed: image patch classification and pixel-wise mosaic image segmentation. The proposed features are compared to four other features: the grayscale intensity, the RGB and CIELab values, and the principal components. Also, three analysis methods are tested: co-occurrence matrix, Gabor filter bank, and local binary pattern. In all cases, the moment features outperformed the opponents. Notably, combining the moment features with complementary attributes remarkably improved the performance. The most discriminative combinations are studied and formulated in this article.
Machine Vision and Applications – Springer Journals
Published: Nov 29, 2017
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