Zobrazeno 1 - 10
of 108
pro vyhledávání: '"Shanwei, Liu"'
Autor:
Muhammad Yasir, Shanwei Liu, Saied Pirasteh, Mingming Xu, Hui Sheng, Jianhua Wan, Felipe A.P. de Figueiredo, Fernando J. Aguilar, Jonathan Li
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 134, Iss , Pp 104137- (2024)
This paper presents a novel approach to tracking ships in Synthetic Aperture Radar (SAR) images based on an improved lightweight YOLOv8 Nano (YOLOv8n), specially devised to improve efficiency without compromising accuracy. In our method, we replaced
Externí odkaz:
https://doaj.org/article/289dd49619934a609b59a66f5e3925e7
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 19575-19587 (2024)
High resolution remote sensing imagery plays a crucial role in monitoring coastal wetlands. Coastal wetland landscapes exhibit diverse features, ranging from fragmented patches to expansive areas. Mainstream convolutional neural networks cannot effec
Externí odkaz:
https://doaj.org/article/6be4bf37581a41a29beec406d8162d98
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 14368-14379 (2024)
The combination of multispectral image (MSI) and synthetic aperture radar (SAR) data has made certain progress in coastal wetland classification. How to realize the interactive fusion between the two data and make full use of their fusion characteris
Externí odkaz:
https://doaj.org/article/d7570ea01c6a442fbe30b7cccdeef6cd
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 4726-4742 (2024)
Target recognition from remote sensing images is commonly challenging because of large-scale variations and small objects, and these challenges are more prominent in satellite video images. The current object detection algorithms have some difficulti
Externí odkaz:
https://doaj.org/article/2229b098c6b64f898b071cbd2faba49e
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 3687-3700 (2024)
Deep learning has gained popularity in hyperspectral unmixing (HU) applications recently due to its powerful learning and data-fitting capabilities. As an unmixing baseline network, the autoencoder (AE) framework performs well in HU by automatically
Externí odkaz:
https://doaj.org/article/cac59d96c179448ca581df2b028946c1
Publikováno v:
Journal of Marine Science and Engineering, Vol 12, Iss 10, p 1786 (2024)
Ship detection faces significant challenges such as dense arrangements, varying dimensions, and interference from the sea surface background. Existing ship detection methods often fail to accurately identify ships in these complex marine environments
Externí odkaz:
https://doaj.org/article/851d0d67dc06424ba9d4689daab16dfd
Autor:
Yasir, Muhammad, Shanwei, Liu, Mingming, Xu, Jianhua, Wan, Nazir, Shah, Islam, Qamar Ul, Dang, Kinh Bac
Publikováno v:
In Applied Soft Computing July 2024 160
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 129, Iss , Pp 103864- (2024)
Hyperspectral unmixing is a key technology in the development of remote sensing applications. However, since both endmembers and abundances are unknown, unmixing is a non-convex problem with a large solution space. To solve this, existing methods usu
Externí odkaz:
https://doaj.org/article/aa7533b7fbf447e1a8b51942e4c8c5f4
Autor:
Hongxia Zheng, Yulin Wu, Haifeng Han, Juan Wang, Shanwei Liu, Mingming Xu, Jianyong Cui, Muhammad Yasir
Publikováno v:
Frontiers in Marine Science, Vol 11 (2024)
Nitrogen is one of the critical factors in water pollution and eutrophication, so applying the deep learning method in remote sensing inversion of nitrogen can provide basic information for environmental management. This paper proposes a two-step fea
Externí odkaz:
https://doaj.org/article/2770ce2baf2f4445b3d2bac774584b59
Autor:
Yasir, Muhammad, Shanwei, Liu, Mingming, Xu, Jianhua, Wan, Hui, Sheng, Nazir, Shah, Zhang, Xin, Tugsan Isiacik Colak, Arife
Publikováno v:
In International Journal of Applied Earth Observation and Geoinformation April 2024 128