Robust control of varying weak hyperspectral target detection with sparse non-negative representation
arXiv:1702.00609 · doi:10.1109/TSP.2017.2688965
Abstract
In this study, a multiple-comparison approach is developed for detecting faint hyperspectral sources. The detection method relies on a sparse and non-negative representation on a highly coherent dictionary to track a spatially varying source. A robust control of the detection errors is ensured by learning the test statistic distributions on the data. The resulting control is based on the false discovery rate, to take into account the large number of pixels to be tested. This method is applied to data recently recorded by the three-dimensional spectrograph Multi-Unit Spectrograph Explorer.
References in corpus (7)
- The MUSE second-generation VLT instrument
- Higher criticism for detecting sparse heterogeneous mixtures
- The MUSE 3D view of the Hubble Deep Field South
- Global testing under sparse alternatives: ANOVA, multiple comparisons and the higher criticism
- Innovated higher criticism for detecting sparse signals in correlated noise
- Higher Criticism for Large-Scale Inference, Especially for Rare and Weak Effects
- Using negative detections to estimate source finder reliability