# Robust control of varying weak hyperspectral target detection with sparse non-negative representation

## TL;DR

This paper introduces a robust hyperspectral source detection method using sparse non-negative representations and false discovery rate control, effectively handling spatially varying faint signals in high-dimensional data.

## Contribution

It develops a novel multiple-comparison detection approach with robust error control tailored for hyperspectral data using sparse, non-negative representations on coherent dictionaries.

## Key findings

- Successfully applied to real Multi-Unit Spectrograph Explorer data
- Achieves reliable detection of faint hyperspectral sources
- Provides controlled false discovery rate in high-dimensional testing

## 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.