Recently, hyperspectral anomaly detection (HAD) methods based on tensor low-rank
representation (TLRR) have received widespread attention. However, most of them tend to …

Hyperspectral anomaly detection (HAD) aims to label each hyperspectral image (HSI) pixel
as background or anomaly, in a totally unsupervised manner. Thus, a fine background …

As one of the most active research hotspots in hyperspectral remote sensing, anomaly
detection is widely used because it takes effect without any priori information about the …

Infrared (IR) small target detection plays an essential role in many civilian and military fields.
Low-rank and sparse decomposition-based detection techniques have gradually shown …

Real-time local anomaly detection needs to be performed simultaneously with hyperspectral
image acquisition. However, the discussion on the scope of using existing data, mainly …

In recent years, tensor representation-based approaches have been widely studied in
hyperspectral anomaly detection. However, these methods still suffer from two key issues …

For scenes with complex backgrounds and weak anomalies, how to effectively distinguish
anomaly targets from the background is the key to perform hyperspectral image (HSI) …

The robustness and accuracy of industrial object pose tracking is critical in manufacturing
automation. Vision-based object tracking methods estimate poses by establishing feature …

Hyperspectral anomaly detection (HAD) aims to recognize a minority of anomalies that are
spectrally different from their surrounding background without prior knowledge. Deep neural …

Hyperspectral anomaly detection (HAD) entails identifying anomaly pixels in hyperspectral
images (HSI) that significantly diverge from the background spectral signatures. However …