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This discussion paper is/has been under review for the journal Biogeosciences (BG). Please refer to the corresponding final paper in BG if available. Abstract Coastal zones are important source regions for a variety of trace gases including halocarbons and sulphur-bearing species. While salt-marshes, macroalgae and phy-toplankton communities have been(More)
In this work, we evaluate the conditionally positive definite logarithmic kernel in kernel-based estimation of reflectance spectra. Reflectance spectra are estimated from responses of a 12-channel multispectral imaging system. We demonstrate the performance of the logarithmic kernel in comparison with the linear and Gaussian kernel using simulated and(More)
In spectral imaging, spatial and spectral information of an image scene are combined. There exist several technologies that allow the acquisition of this kind of data. Depending on the optical components used in the spectral imaging systems, misalignment between image channels can occur. Further, the projection of some systems deviates from that of a(More)
The performance of learning-based spectral estimation is greatly influenced by the set of training samples selected to create the reconstruction model. Training sample selection schemes can be categorized into global and local approaches. Most of the previously proposed global training schemes aim to reduce the number of training samples, or a selection of(More)
We have analyzed the performance of simulated multispectral systems for the spectral recovery of reflec-tance of printer inks from camera responses, including noise. To estimate reflectance we compared the performance of four algorithms which were not comparatively tested using the same data sets before. The criteria for selection of the algorithms were(More)
Estimating spectral reflectance has attracted extensive research efforts in color science and machine learning, motivated through a wide range of applications. In many practical situations, prior knowledge is available that ought to be used. Here, we have developed a general Bayesian method that allows the incorporation of prior knowledge from previous(More)