Spatio-spectral binary patterns based on multispectral filter arrays for texture classification.

To discriminate gray-level texture images, spatial texture descriptors can be extracted using the local binary pattern (LBP) operator. This operator has been extended to color images at the expense of increased memory and computation requirements. Some authors propose to compute texture descriptors directly from raw images provided through a Bayer color filter array, which both avoids the demosaicking step and reduces the descriptor size. Recently, multispectral snapshot cameras have emerged to sample more than three wavelength bands using a multispectral filter array. Such cameras provide a raw image in which a single spectral channel value is available at each pixel. In this paper we design a local binary pattern operator that jointly extracts the spatial and spectral texture information directly from a raw image. Extensive experiments on a large dataset show that the proposed descriptor has both reduced computation cost and high discriminative power with regard to classical LBP descriptors applied to demosaicked images.