A Deep Learning Method for Cultivated Land Parcels’ (CLPs) Delineation From High-Resolution Remote Sensing Images With High-Generalization Capability-Reference-Cited by-同舟云学术

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A Deep Learning Method for Cultivated Land Parcels’ (CLPs) Delineation From High-Resolution Remote Sensing Images With High-Generalization Capability

Published:2024 Issue: Volume:62 Page:1-25
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Zhu Yu¹^ORCID,Pan Yaozhong¹^ORCID,Zhang Dujuan²,Wu Hanyi¹^ORCID,Zhao Chuanwu¹^ORCID

Affiliation:

1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, China

2. National Supercomputing Center in Zhengzhou, Zhengzhou University, Zhengzhou, China

Funder

National Natural Science Foundation of China

National High Resolution Earth Observation System Technology Projects of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10589602.pdf?arnumber=10589602

Reference70 articles.

1. Delineation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping

2. Automated crop field extraction from multi-temporal Web Enabled Landsat Data

3. A robust index to extract paddy fields in cloudy regions from SAR time series

4. An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery

5. A Combined Random Forest and OBIA Classification Scheme for Mapping Smallholder Agriculture at Different Nomenclature Levels Using Multisource Data (Simulated Sentinel-2 Time Series, VHRS and DEM)

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