Research on a novel extraction method using Deep Learning based on GF-2 images for aquaculture areas-Reference-Cited by-同舟云学术

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Research on a novel extraction method using Deep Learning based on GF-2 images for aquaculture areas

Published:2020-01-09 Issue:9 Volume:41 Page:3575-3591
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Cheng Bo¹,Liang Chenbin¹²,Liu Xunan³,Liu Yueming¹⁴,Ma Xiaoxiao¹,Wang Guizhou¹

Affiliation:

1. Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences , Beijing, China

2. Institute of Automation Chinese Academy of Sciences , China

3. National Marine Hazard Mitigation Service , Beijing, China

4. Institute of Geographic Sciences and Natural Resource Research , Beijing, China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2019.1706009

Reference25 articles.

1. A Novel Connectionist System for Unconstrained Handwriting Recognition

2. Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation

3. A Fast Learning Algorithm for Deep Belief Nets

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1. MSSFNet: A Multiscale Spatial–Spectral Fusion Network for Extracting Offshore Floating Raft Aquaculture Areas in Multispectral Remote Sensing Images;Sensors;2024-08-12

2. Extraction of offshore aquaculture areas from Gaofen-2 remote sensing imagery based on deep learning;Remote Sensing Letters;2024-06-19

3. Marine Infrastructure Detection with Satellite Data—A Review;Remote Sensing;2024-05-09

4. Semantic scene segmentation for indoor autonomous vision systems: leveraging an enhanced and efficient U-NET architecture;Multimedia Tools and Applications;2024-05-09

5. Mapping the distribution and dynamics of coastal aquaculture ponds using Landsat time series data based on U ² -Net deep learning model;International Journal of Digital Earth;2024-04-24

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