Visualization of spectral images

Spectral image sensors provide images with a large number of contiguous spectral channels per pixel. Visualization of these huge data sets is not a straightforward issue. There are three principal ways in which spectral data can be presented; as spectra, as image and in feature space. This paper describes several visualization methods and their suitability in the different steps in the research cycle. Combinations of the three presentation methods and dynamic interaction between them, adds significant to the usability. Examples of some software implementations are given. Also the application of volume visualization methods to display spectral images is shown to be valuabl

Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution

In many computer vision applications, obtaining images of high resolution in both the spatial and spectral domains are equally important. However, due to hardware limitations, one can only expect to acquire images of high resolution in either the spatial or spectral domains. This paper focuses on hyperspectral image super-resolution (HSI-SR), where a hyperspectral image (HSI) with low spatial resolution (LR) but high spectral resolution is fused with a multispectral image (MSI) with high spatial resolution (HR) but low spectral resolution to obtain HR HSI. Existing deep learning-based solutions are all supervised that would need a large training set and the availability of HR HSI, which is unrealistic. Here, we make the first attempt to solving the HSI-SR problem using an unsupervised encoder-decoder architecture that carries the following uniquenesses. First, it is composed of two encoder-decoder networks, coupled through a shared decoder, in order to preserve the rich spectral information from the HSI network. Second, the network encourages the representations from both modalities to follow a sparse Dirichlet distribution which naturally incorporates the two physical constraints of HSI and MSI. Third, the angular difference between representations are minimized in order to reduce the spectral distortion. We refer to the proposed architecture as unsupervised Sparse Dirichlet-Net, or uSDN. Extensive experimental results demonstrate the superior performance of uSDN as compared to the state-of-the-art.Comment: Accepted by The IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018, Spotlight

Mathematical methods for anomaly grouping in hyperspectral images

The topological anomaly detection (TAD) algorithm differs from other anomaly detection algorithms in that it does not rely on the data\u27s being normally distributed. We have built on this advantage of TAD by extending the algorithm so that it gives a measure of the number of anomalous objects, rather than the number of anomalous pixels, in a hyperspectral image. We have done this by identifying and integrating clusters of anomalous pixels, which we accomplished with a graph-theoretical method that combines spatial and spectral information. By applying our method, the Anomaly Clustering algorithm, to hyperspectral images, we have found that our method integrates small clusters of anomalous pixels, such as those corresponding to rooftops, into single anomalies; this improves visualization and interpretation of objects. We have also performed a local linear embedding (LLE) analysis of the TAD results to illustrate its application as a means of grouping anomalies together. By performing the LLE algorithm on just the anomalies identified by the TAD algorithm, we drastically reduce the amount of computation needed for the computationally-heavy LLE algorithm. We also propose an application of a shifted QR algorithm to improve the speed of the LLE algorithm