# Sparse and Low-Rank Matrix Decomposition for Automatic Target Detection in Hyperspectral Imagery

arXiv: https://arxiv.org/abs/1711.08970 · HTML: https://mlsanity.com/paper/1711.08970/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2017.

## Built on by (most cited first, 3 of 3)

- [Binary Change Guided Hyperspectral Multiclass Change Detection](https://mlsanity.com/paper/2112.04493/) (2021)
- [A spectral-spatial fusion anomaly detection method for hyperspectral imagery](https://mlsanity.com/paper/2202.11889/) (2022)
- [Deep Learning-Based Anomaly Detection in Synthetic Aperture Radar Imaging](https://mlsanity.com/paper/2210.16038/) (2022)

## Abstract (arXiv)

Given a target prior information, our goal is to propose a method for automatically separating targets of interests from the background in hyperspectral imagery. More precisely, we regard the given hyperspectral image (HSI) as being made up of the sum of low-rank background HSI and a sparse target HSI that contains the targets based on a pre-learned target dictionary constructed from some online spectral libraries. Based on the proposed method, two strategies are briefly outlined and evaluated to realize the target detection on both synthetic and real experiments.