Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Xiangyong Cao"'
Autor:
Zhongyuan JIANG, Sen WANG, Qizhou WANG, Xiangyong CAO, Xingsong HOU, Lei LEI, Dongwei SUN, Xinghua LI, Jianfeng MA
Publikováno v:
天地一体化信息网络, Vol 5, Pp 60-75 (2024)
Firstly, the current situation of satellite remote sensing, communication, and computing systems was systematically analyzed, and the real-time remote sensing dilemma existing in the traditional satellite remote sensing systems and the satellite remo
Externí odkaz:
https://doaj.org/article/ea515607031e49ccb93ec5cd15fea5bc
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 18727-18738 (2024)
Remote sensing image change captioning (RSICC) aims to automatically generate sentences that describe content differences in remote sensing bitemporal images. Recently, attention-based transformers have become a prevalent idea for capturing the featu
Externí odkaz:
https://doaj.org/article/9240d299300c4696b6eb001586680ee0
Publikováno v:
Remote Sensing, Vol 16, Iss 21, p 3979 (2024)
Multispectral and hyperspectral image fusion (MS/HS fusion) aims to generate a high-resolution hyperspectral (HRHS) image by fusing a high-resolution multispectral (HRMS) and a low-resolution hyperspectral (LRHS) images. The deep unfolding-based MS/H
Externí odkaz:
https://doaj.org/article/ddb6841fcf2e4b208d4017f88f8338ae
Publikováno v:
Remote Sensing, Vol 16, Iss 13, p 2499 (2024)
Due to the intricate terrain and restricted resources, hyperspectral image (HSI) datasets captured in real-world scenarios typically contain noisy labels, which may seriously affect the classification results. To address this issue, we work on a univ
Externí odkaz:
https://doaj.org/article/9832991a6f9b4418bedefcbd162a83b1
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 10230-10245 (2023)
The global context is crucial to the semantic segmentation task of remote sensing (RS) urban scene imagery since objects have large size variations, high similarity, and mutual occlusion. However, the existing methods for extracting global context in
Externí odkaz:
https://doaj.org/article/9efa5adbf9de4d44b2df77a4ad559c23
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 5189-5203 (2023)
Hyperspectral image (HSI) denoising based on nonlocal subspace representation has attracted a lot of attention recently. However, most of the existing works mainly focus on refining the representation coefficient images (RCIs) using certain nonlocal
Externí odkaz:
https://doaj.org/article/692474c1833240eca521c09b00ca9db2
Publikováno v:
Remote Sensing, Vol 15, Iss 11, p 2869 (2023)
Based on deep learning, various pan-sharpening models have achieved excellent results. However, most of them adopt simple addition or concatenation operations to merge the information of low spatial resolution multi-spectral (LRMS) images and panchro
Externí odkaz:
https://doaj.org/article/f24f56cbb668473ab5c0cc9ca84f1011
Publikováno v:
Remote Sensing, Vol 15, Iss 8, p 1970 (2023)
Deep image prior (DIP) is a powerful technique for image restoration that leverages an untrained network as a handcrafted prior. DIP can also be used for hyperspectral image (HSI) denoising tasks and has achieved impressive performance. Recent works
Externí odkaz:
https://doaj.org/article/79a35f03e93f4e7fa21d482a1d37b001
Publikováno v:
Remote Sensing, Vol 14, Iss 18, p 4598 (2022)
We propose a new deep neural network termed TRQ3DNet which combines convolutional neural network (CNN) and transformer for hyperspectral image (HSI) denoising. The network consists of two branches. One is built by 3D quasi-recurrent blocks, including
Externí odkaz:
https://doaj.org/article/426b6d6a7d974a688988333514a230f3
Publikováno v:
Remote Sensing, Vol 11, Iss 13, p 1565 (2019)
In this paper, a new supervised classification algorithm which simultaneously considers spectral and spatial information of a hyperspectral image (HSI) is proposed. Since HSI always contains complex noise (such as mixture of Gaussian and sparse noise
Externí odkaz:
https://doaj.org/article/708beb8986794f75a01634a088353763