Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Huang, Zhongling"'
SAR image simulation has attracted much attention due to its great potential to supplement the scarce training data for deep learning algorithms. Consequently, evaluating the quality of the simulated SAR image is crucial for practical applications. T
Externí odkaz:
http://arxiv.org/abs/2407.19436
Most existing sparse representation-based approaches for attributed scattering center (ASC) extraction adopt traditional iterative optimization algorithms, which suffer from lengthy computation times and limited precision. This paper presents a solut
Externí odkaz:
http://arxiv.org/abs/2405.09073
There has been a recent emphasis on integrating physical models and deep neural networks (DNNs) for SAR target recognition, to improve performance and achieve a higher level of physical interpretability. The attributed scattering center (ASC) paramet
Externí odkaz:
http://arxiv.org/abs/2309.15697
The recognition or understanding of the scenes observed with a SAR system requires a broader range of cues, beyond the spatial context. These encompass but are not limited to: imaging geometry, imaging mode, properties of the Fourier spectrum of the
Externí odkaz:
http://arxiv.org/abs/2301.03589
Autor:
Huang, Zhongling, Yao, Xiwen, Liu, Ying, Dumitru, Corneliu Octavian, Datcu, Mihai, Han, Junwei
Publikováno v:
ISPRS Journal of Photogrammetry and Remote Sensing Volume 190, August 2022, Pages 25-37
Integrating the special electromagnetic characteristics of Synthetic Aperture Radar (SAR) in deep neural networks is essential in order to enhance the explainability and physics awareness of deep learning. In this paper, we first propose a novel phys
Externí odkaz:
http://arxiv.org/abs/2110.14144
Publikováno v:
In ISPRS Journal of Photogrammetry and Remote Sensing January 2024 207:164-174
Publikováno v:
IEEE Geoscience and Remote Sensing Letters 2020
The classification of large-scale high-resolution SAR land cover images acquired by satellites is a challenging task, facing several difficulties such as semantic annotation with expertise, changing data characteristics due to varying imaging paramet
Externí odkaz:
http://arxiv.org/abs/2001.01425
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing 2019
Deep convolutional neural networks (DCNNs) have attracted much attention in remote sensing recently. Compared with the large-scale annotated dataset in natural images, the lack of labeled data in remote sensing becomes an obstacle to train a deep net
Externí odkaz:
http://arxiv.org/abs/1906.01379
Autor:
Xue, Wenhao, Yang, Yang, Li, Lei, Huang, Zhongling, Wang, Xinggang, Han, Junwei, Zhang, Dingwen
Publikováno v:
CAAI Transactions on Intelligence Technology; Jun2024, Vol. 9 Issue 3, p695-708, 14p
Publikováno v:
In ISPRS Journal of Photogrammetry and Remote Sensing March 2020 161:179-193