A refined edge-aware convolutional neural networks for agricultural parcel delineation-Reference-Cited by-同舟云学术

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A refined edge-aware convolutional neural networks for agricultural parcel delineation

Published:2024-09 Issue: Volume:133 Page:104084
ISSN:1569-8432
Container-title:International Journal of Applied Earth Observation and Geoinformation
language:en
Short-container-title:International Journal of Applied Earth Observation and Geoinformation

Author:

Lu Rui,Zhang Yingfan,Huang Qiting,Zeng Penghao,Shi Zhou,Ye Su

Funder

Ministry of Science and Technology of the People's Republic of China

Ministry of Education of the People's Republic of China

National Key Research and Development Program of China

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Reference50 articles.

1. A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach;Cai;Remote Sens. Environ.,2018

2. Improving agricultural field parcel delineation with a dual branch spatiotemporal fusion network by integrating multimodal satellite data;Cai;ISPRS J. Photogramm. Remote Sens.,2023

3. Learning spectral-spatial representations from VHR images for fine-scale crop type mapping: A case study of rice-crayfish field extraction in South China;Cai;ISPRS J. Photogramm. Remote Sens.,2023

4. Contour Detection for UAV-Based Cadastral Mapping;Crommelinck;Remote Sens.,2017

5. ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data;Diakogiannis;ISPRS J. Photogramm. Remote Sens.,2020

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