Single object tracking (SOT) in satellite video (SV) enables the continuous acquisition of
position and range information of an arbitrary object, showing promising value in remote …

High-dimensional and complex spectral structures make the clustering of hyperspectral
images (HSIs) a challenging task. Subspace clustering is an effective approach for …

Hyperspectral video contains rich spectral, spatial, and temporal conditions that are crucial
for capturing complex object variations and overcoming the inherent limitations (eg, multi …

Hyperspectral image (HSI) clustering is a fundamental yet challenging task that groups
image pixels with similar features into distinct clusters. Among various approaches …

Satellite video is an emerging surface observation data that has drawn increasing interest
due to its potential in spatiotemporal dynamic analysis. Single object tracking of satellite …

Hyperspectral video offers a wealth of material and motion cues about objects. This
advantage proves invaluable in addressing the inherent limitations of generic RGB video …

Hyperspectral anomaly detection (HAD) is a challenging task since it identifies the anomaly
targets without prior knowledge. In recent years, deep learning methods have emerged as …

Remote sensing haze removal is a popular computational imaging technique that directly
obtains clear remote sensing data from hazy remote sensing images. Apart from prior-based …

Hyperspectral images (HSIs) with rich spectral information have been widely used in surface
classification, object detection, and other real application problems. However, due to the …

Hyperspectral (HS) video captures continuous spectral information of objects, enhancing
material identification in tracking tasks. It is expected to overcome the inherent limitations of …