Hyperspectral Anomaly Detection with Data Sphering and Unsupervised Target Detection

Low rank and sparse representation (LRaSR)-based approaches have been widely used for hyperspectral anomaly detection (HAD) by effectively decomposing a hyperspectral data set into a low - rank component for background (BKG), a sparse component for anomalies, and a residual component for noise. This paper proposed a novel hyperspectral anomaly detection (AD) method based on data sphering (DS) and unsupervised target detection with sparse cardinality (DS-UTSSC). First of all, DS-UTS uses data sphering to remove BKG, which is characterized by 1 st and 2
^nd
statistics, for original data
$X$
. Second, potential anomaly component
$\mathrm{S}_{\text{DS}-\mathrm{U}\text{TS}}$
is generated via unsupervised target detection and subspace projection for the sphered data
$X$
. To further reduce the impact of noise on anomaly detection, sparse cardinality (SC) is incorporated to obtain
$\mathrm{S}_{\text{DS}-\text{UTSSC}}$
. Finally, RX-AD is implemented on
$\mathrm{S}_{\mathrm{D}\text{S-UTSSC}}$
to detect anomalies. The experimental results validate that DS-UTSSC is very competitive against the LRaSR-based models and AE-based method.