Customizing kernel functions for SVM-based hyperspectral image classification

Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available algorithms. However, few efforts have been made to extend SVMs to cover the specific requirements of hyperspectral image classification, for example, by building tailor-made kernels. Observation of real-life spectral imagery from the AVIRIS hyperspectral sensor shows that the useful information for classification is not equally distributed across bands, which provides potential to enhance the SVM's performance through exploring different kernel functions. Spectrally weighted kernels are, therefore, proposed, and a set of particular weights is chosen by either optimizing an estimate of generalization error or evaluating each band's utility level. To assess the effectiveness of the proposed method, experiments are carried out on the publicly available 92AV3C dataset collected from the 220-dimensional AVIRIS hyperspectral sensor. Results indicate that the method is generally effective in improving performance: spectral weighting based on learning weights by gradient descent is found to be slightly better than an alternative method based on estimating ";relevance"; between band information and ground trut

Unmasking Clever Hans Predictors and Assessing What Machines Really Learn

Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly "intelligent" behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem solving behaviors. Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for. Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner.Comment: Accepted for publication in Nature Communication

Comment on “Temporal and spatial variation in harbor seal (Phoca vitulina L.) roar calls from southern Scandinavia” [J. Acoust. Soc. Am. 141, 1824-1834 (2017)]

In their recent article, Sabinsky and colleagues investigated heterogeneity in harbor seals' vocalizations. The authors found seasonal and geographical variation in acoustic parameters, warning readers that recording conditions might account for some of their results. This paper expands on the temporal aspect of the encountered heterogeneity in harbor seals' vocalizations. Temporal information is the least susceptible to variable recording conditions. Hence geographical and seasonal variability in roar timing constitutes the most robust finding in the target article. In pinnipeds, evidence of timing and rhythm in the millisecond range—as opposed to circadian and seasonal rhythms—has theoretical and interdisciplinary relevance. In fact, the study of rhythm and timing in harbor seals is particularly decisive to support or confute a cross-species hypothesis, causally linking the evolution of vocal production learning and rhythm. The results by Sabinsky and colleagues can shed light on current scientific questions beyond pinniped bioacoustics, and help formulate empirically testable predictions

Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/ licenses/by/4.0