Target detection in hyperspectral remote sensing image: Current status and challenges
Abundant spectral information endows unique advantages of hyperspectral remote sensing
images in target location and recognition. Target detection techniques locate materials or …
images in target location and recognition. Target detection techniques locate materials or …
Spatial–Temporal Federated Transfer Learning with multi-sensor data fusion for cooperative positioning
With the development of advanced embedded and communication systems, location
information has become a crucial factor in supporting context-aware or location-aware …
information has become a crucial factor in supporting context-aware or location-aware …
Anomaly detection of hyperspectral image with hierarchical antinoise mutual-incoherence-induced low-rank representation
Hyperspectral image (HSI) anomaly detection (AD) generally considers background pixels
as low-rank distribution and anomaly pixels as sparse distribution. However, it is usually …
as low-rank distribution and anomaly pixels as sparse distribution. However, it is usually …
A multidepth and multibranch network for hyperspectral target detection based on band selection
Deep learning (DL) has recently risen to prominence in hyperspectral target detection
(HTD). Nevertheless, how to tackle the extreme training sample imbalance together with …
(HTD). Nevertheless, how to tackle the extreme training sample imbalance together with …
Automatic counterfeit currency detection using a novel snapshot hyperspectral imaging algorithm
In this study, a snapshot-based hyperspectral imaging (HSI) algorithm that converts RGB
images to HSI images is designed using the Raspberry Pi environment. A Windows-based …
images to HSI images is designed using the Raspberry Pi environment. A Windows-based …
[HTML][HTML] Self-supervised learning with deep clustering for target detection in hyperspectral images with insufficient spectral variation prior
X Zhang, K Gao, J Wang, Z Hu, H Wang, P Wang… - International Journal of …, 2023 - Elsevier
Target detection in hyperspectral images (HSIs) mainly relies on the spectral information of
the target prior. However, prior spectra with precise variation information are often hard to …
the target prior. However, prior spectra with precise variation information are often hard to …
Generative Self supervised Learning with Spectral spatial Masking for Hyperspectral Target Detection
Deep learning (DL) has made significant progress in hyperspectral target detection (HTD) in
recent years. However, the existing DL-based HTD methods generally generate numerous …
recent years. However, the existing DL-based HTD methods generally generate numerous …
Distillation-constrained prototype representation network for hyperspectral image incremental classification
C Yu, X Zhao, B Gong, Y Hu, M Song… - … on Geoscience and …, 2024 - ieeexplore.ieee.org
Oriented to adaptive recognition of the new land-cover categories, incremental classification
(IC) that aims to complete adaptive classification with continuous learning is urgent and …
(IC) that aims to complete adaptive classification with continuous learning is urgent and …
Anomaly detection for hyperspectral imagery via tensor low-rank approximation with multiple subspace learning
X He, J Wu, Q Ling, Z Li, Z Lin… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Hyperspectral anomaly detection (HAD) is regarded as an indispensable, pivotal technology
in remote sensing and Earth science domains. Nevertheless, most existing detection …
in remote sensing and Earth science domains. Nevertheless, most existing detection …
Learnable background endmember with subspace representation for hyperspectral anomaly detection
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 background or anomaly, in a totally unsupervised manner. Thus, a fine background …