Satellite video single object tracking: A systematic review and an oriented object tracking benchmark
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 …
position and range information of an arbitrary object, showing promising value in remote …
Contrastive multi-view subspace clustering of hyperspectral images based on graph convolutional networks
High-dimensional and complex spectral structures make the clustering of hyperspectral
images (HSIs) a challenging task. Subspace clustering is an effective approach for …
images (HSIs) a challenging task. Subspace clustering is an effective approach for …
[HTML][HTML] REPS: Rotation equivariant Siamese network enhanced by probability segmentation for satellite video tracking
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 …
due to its potential in spatiotemporal dynamic analysis. Single object tracking of satellite …
SENSE: Hyperspectral video object tracker via fusing material and motion cues
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 …
advantage proves invaluable in addressing the inherent limitations of generic RGB video …
Deep feature aggregation network for hyperspectral anomaly detection
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 …
targets without prior knowledge. In recent years, deep learning methods have emerged as …
PhDnet: A novel physic-aware dehazing network for remote sensing images
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 …
obtains clear remote sensing data from hazy remote sensing images. Apart from prior-based …
Differentiable modeling for soil moisture retrieval by unifying deep neural networks and water cloud model
Abstract Machine learning has been widely used in high-spatial-resolution surface soil
moisture (SSM) retrieval studies, but in recent years, this purely data-driven retrieval method …
moisture (SSM) retrieval studies, but in recent years, this purely data-driven retrieval method …
An Ensemble Learning Approach With Attention Mechanism for Detecting Pavement Distress and Disaster-Induced Road Damage
Road damage presents a significant risk to traffic safety, including pavement distress and
disaster-induced damage. Thanks to their high efficiency, computer vision-based methods …
disaster-induced damage. Thanks to their high efficiency, computer vision-based methods …
Hyperspectral Anomaly Detection via Low-Rank Representation with Dual Graph Regularizations and Adaptive Dictionary
In a hyperspectral image, there is a close correlation between spectra and a certain degree
of correlation in the pixel space. However, most existing low-rank representation (LRR) …
of correlation in the pixel space. However, most existing low-rank representation (LRR) …
PCDASNet: Position-Constrained Differential Attention Siamese Network for Building Damage Assessment
Sudden natural disasters and man-made disasters pose a threat to human life and property
safety, and real-time semantic segmentation of high-resolution remote sensing images is …
safety, and real-time semantic segmentation of high-resolution remote sensing images is …