Hierarchical Beta Process with Gaussian Process Prior for Hyperspectral Image Super Resolution
Hyperspectral cameras acquire precise spectral information, however, their resolution is very low due to hardware constraints. We propose an image fusion based hyperspectral super resolution approach that employes a Bayesian representation model. The prop
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School of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Highway, 6009 Crawley, WA, Australia [email protected], [email protected] 2 School of Electrical Engineering and Computer Science, National University of Sciences and Technology, H-12, Islamabad, Pakistan [email protected]
Abstract. Hyperspectral cameras acquire precise spectral information, however, their resolution is very low due to hardware constraints. We propose an image fusion based hyperspectral super resolution approach that employes a Bayesian representation model. The proposed model accounts for spectral smoothness and spatial consistency of the representation by using Gaussian Processes and a spatial kernel in a hierarchical formulation of the Beta Process. The model is employed by our approach to first infer Gaussian Processes for the spectra present in the hyperspectral image. Then, it is used to estimate the activity level of the inferred processes in a sparse representation of a high resolution image of the same scene. Finally, we use the model to compute multiple sparse codes of the high resolution image, that are merged with the samples of the Gaussian Processes for an accurate estimate of the high resolution hyperspectral image. We perform experiments with remotely sensed and ground-based hyperspectral images to establish the effectiveness of our approach.
Keywords: Hyperspectral
1
· Super-resolution · Beta/Gaussian Process
Introduction
Spectral characteristics of materials are considered vital in remote sensing, medical imaging and forensics [1–6]. Recently, they have also shown improved performance in various computer vision tasks, e.g. recognition [7–9], document analysis [10,11], tracking [12], pedestrian detection [13] and segmentation [14]. Hyperspectral imaging is an emerging modality that can efficiently obtain highfidelity spectral representations of a scene. Nevertheless, the low resolution of contemporary hyperspectral cameras is currently a bottleneck in its ubiquitous use [4,15,16]. Reflectance spectra are characterized by their intensity distributions over continuous wavelength ranges. Hence, hyperspectral cameras integrate scene c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part III, LNCS 9907, pp. 103–120, 2016. DOI: 10.1007/978-3-319-46487-9 7
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radiance with hundreds of spectrally sharp bases, thereby requiring longer exposures. This results in a reduced resolution image. Moreover, it is not straightforward to use high resolution sensors in hyperspectral cameras because they
Fig. 1. Schematics: Using the proposed model, a set of Gaussian Processes (GPs) is inferred for the spectra in hyperspectral image. The means of the GPs are transformed according to the spectral channels of a high resolution image X of the same scene. The transformed means and X are used to compute a set B of Bernoulli distributions, signifying the activity level of GPs in the sparse codes of X. Multiple sparse codes of
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