Zobrazeno 1 - 10
of 3 110
pro vyhledávání: '"Du, Qian"'
Hyperspectral image (HSI) denoising is critical for the effective analysis and interpretation of hyperspectral data. However, simultaneously modeling global and local features is rarely explored to enhance HSI denoising. In this letter, we propose a
Externí odkaz:
http://arxiv.org/abs/2403.10067
Hyperspectral video (HSV) offers valuable spatial, spectral, and temporal information simultaneously, making it highly suitable for handling challenges such as background clutter and visual similarity in object tracking. However, existing methods pri
Externí odkaz:
http://arxiv.org/abs/2403.05852
Masked image modeling (MIM) is a highly popular and effective self-supervised learning method for image understanding. Existing MIM-based methods mostly focus on spatial feature modeling, neglecting spectral feature modeling. Meanwhile, existing MIM-
Externí odkaz:
http://arxiv.org/abs/2311.04442
Synthetic aperture radar (SAR) image change detection is a critical task and has received increasing attentions in the remote sensing community. However, existing SAR change detection methods are mainly based on convolutional neural networks (CNNs),
Externí odkaz:
http://arxiv.org/abs/2309.12010
In the paper, we study the non-Hermitian system under dissipation and give the effective 2*2 Hamiltonian in the k-space by reducing the N*N Hamiltonian in the real space for them. It is discovered that the energy band shows an imaginary line gap. To
Externí odkaz:
http://arxiv.org/abs/2307.14340
Traditionally, numerical models have been deployed in oceanography studies to simulate ocean dynamics by representing physical equations. However, many factors pertaining to ocean dynamics seem to be ill-defined. We argue that transferring physical k
Externí odkaz:
http://arxiv.org/abs/2304.09376
Removing the noise and improving the visual quality of hyperspectral images (HSIs) is challenging in academia and industry. Great efforts have been made to leverage local, global or spectral context information for HSI denoising. However, existing me
Externí odkaz:
http://arxiv.org/abs/2304.09373
The joint hyperspectral image (HSI) and LiDAR data classification aims to interpret ground objects at more detailed and precise level. Although deep learning methods have shown remarkable success in the multisource data classification task, self-supe
Externí odkaz:
http://arxiv.org/abs/2301.03335
Accurately and timely detecting multiscale small objects that contain tens of pixels from remote sensing images (RSI) remains challenging. Most of the existing solutions primarily design complex deep neural networks to learn strong feature representa
Externí odkaz:
http://arxiv.org/abs/2209.13351
Currently, cross-scene hyperspectral image (HSI) classification has drawn increasing attention. It is necessary to train a model only on source domain (SD) and directly transferring the model to target domain (TD), when TD needs to be processed in re
Externí odkaz:
http://arxiv.org/abs/2209.01634