Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Zheheng Liang"'
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 118, Iss , Pp 103279- (2023)
Deep learning-based fire detection models are usually trained offline on static datasets. For continuously increasing heterogeneous sensor data, incremental learning is a resolution to enable incremental updates of models. However, it still encounter
Externí odkaz:
https://doaj.org/article/fe330d22916945229fa45df2ae2f917e
Autor:
Fan Gao, Peng Yue, Zhipeng Cao, Shuaifeng Zhao, Boyi Shangguan, Liangcun Jiang, Lei Hu, Zhe Fang, Zheheng Liang
Publikováno v:
International Journal of Geographical Information Science. 36:1853-1884
Publikováno v:
Automation in Construction. 152:104917
Publikováno v:
2022 4th International Academic Exchange Conference on Science and Technology Innovation (IAECST).
Publikováno v:
2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ).
Publikováno v:
2022 IEEE 5th International Conference on Information Systems and Computer Aided Education (ICISCAE).
Publikováno v:
2022 International Conference on Artificial Intelligence in Everything (AIE).
Autor:
Chenxiao Zhang, Yukang Feng, Lei Hu, Deodato Tapete, Li Pan, Zheheng Liang, Francesca Cigna, Peng Yue
Publikováno v:
International journal of applied earth observation and geoinformation 109 (2022): 102769. doi:10.1016/j.jag.2022.102769
info:cnr-pdr/source/autori:Chenxiao Zhang; Yukang Feng; Lei Hu; Deodato Tapete; Li Pan; Zheheng Liang; Francesca Cigna; Peng Yue/titolo:A domain adaptation neural network for change detection with heterogeneous optical and SAR remote sensing images/doi:10.1016%2Fj.jag.2022.102769/rivista:International journal of applied earth observation and geoinformation/anno:2022/pagina_da:/pagina_a:102769/intervallo_pagine:102769/volume:109
info:cnr-pdr/source/autori:Chenxiao Zhang; Yukang Feng; Lei Hu; Deodato Tapete; Li Pan; Zheheng Liang; Francesca Cigna; Peng Yue/titolo:A domain adaptation neural network for change detection with heterogeneous optical and SAR remote sensing images/doi:10.1016%2Fj.jag.2022.102769/rivista:International journal of applied earth observation and geoinformation/anno:2022/pagina_da:/pagina_a:102769/intervallo_pagine:102769/volume:109
Heterogeneous remote sensing source-based change detection with optical and SAR data and their combined all-time and all-weather observation capability provides a reliable and promising solution for a wide range of applications. State-of-the-art supe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e1fb26c464f0ec0c8a8b4763f94b06e
http://www.cnr.it/prodotto/i/466689
http://www.cnr.it/prodotto/i/466689
Publikováno v:
International Journal of Applied Earth Observation and Geoinformation. 111:102830
Publikováno v:
2020 the 3rd International Conference on Control and Computer Vision.
Face recognition has attracted wide attention in recent years. The major issue in face recognition is to improve the robustness under various modalities, such as different illuminations, poses, or backgrounds. Due to the rapid development of deep lea