Výsledky vyhledávání

Akademický článek

Satellite remote sensing and bathymetry co-driven deep neural network for coral reef shallow water benthic habitat classification

Autor: Hui Chen, Jian Cheng, Xiaoguang Ruan, Jizhe Li, Li Ye, Sensen Chu, Liang Cheng, Ka Zhang

Publikováno v: International Journal of Applied Earth Observations and Geoinformation, Vol 132, Iss , Pp 104054- (2024)

Shallow-water benthic habitat classification of coral reefs based on satellite remote sensing is an important part of coral reef monitoring. Leveraging its potent capacity for feature learning, and generalization, deep learning emerges as a robust me

Externí odkaz: https://doaj.org/article/84fce8e5cb284931b3c5a244f54b46c7

Zobrazit plný text záznamu

Akademický článek

Land-sea classification based on the fast feature detection model for ICESat-2 ATL03 datasets

Autor: Jizhe Li, Sensen Chu, Qixin Hu, Yu Cong, Jian Cheng, Hui Chen, Liang Cheng, Guoping Zhang, Shuai Xing

Publikováno v: International Journal of Applied Earth Observations and Geoinformation, Vol 130, Iss , Pp 103916- (2024)

Accurate land-sea classification of ATL03 datasets is the prerequisite for signal photons identification and higher-level data production. Current photon classification methods are mainly based on physical models and machine learning methods. Physica

Externí odkaz: https://doaj.org/article/6aad2fccb76445d0b833325bb1f505a2

Zobrazit plný text záznamu

Akademický článek

FSPN: End-to-end full-space pooling weakly supervised network for benthic habitat mapping using remote sensing images

Autor: Hui Chen, Sensen Chu, Qizhi Zhuang, Zhixin Duan, Jian Cheng, Jizhe Li, Li Ye, Jun Yu, Liang Cheng

Publikováno v: International Journal of Applied Earth Observations and Geoinformation, Vol 118, Iss , Pp 103264- (2023)

Space remote sensing is powerful for conducting large-scale coral reefs benthic habitat mapping (CRBHM). However, it is difficult because of the large spectral differences in remote sensing images of benthic habitats due to the environment, observati

Externí odkaz: https://doaj.org/article/a1d203cdc9cc40b69459488cddb352f8

Zobrazit plný text záznamu

Akademický článek

A Comprehensive Evaluation of Machine Learning and Classical Approaches for Spaceborne Active-Passive Fusion Bathymetry of Coral Reefs

Autor: Jian Cheng, Liang Cheng, Sensen Chu, Jizhe Li, Qixin Hu, Li Ye, Zhiyong Wang, Hui Chen

Publikováno v: ISPRS International Journal of Geo-Information, Vol 12, Iss 9, p 381 (2023)

Satellite-derived bathymetry (SDB) techniques are increasingly valuable for deriving high-quality bathymetric maps of coral reefs. Investigating the performance of the related SDB algorithms in purely spaceborne active–passive fusion bathymetry con

Externí odkaz: https://doaj.org/article/b224a0f2e1cf40468e98cfa29fce12bd

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání