Zobrazeno 1 - 10
of 1 411
pro vyhledávání: '"Crop classification"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract Crop classification using remote sensing technology is highly important for monitoring agricultural resources and managing water usage, especially in water-scarce regions like the Hei River. Crop classification requires a substantial number
Externí odkaz:
https://doaj.org/article/c349053028564af186b9a2d7a6723914
Autor:
Hengbin Wang, Zijing Ye, Yu Yao, Wanqiu Chang, Junyi Liu, Yuanyuan Zhao, Shaoming Li, Zhe Liu, Xiaodong Zhang
Publikováno v:
Geo-spatial Information Science, Pp 1-16 (2024)
Cross-Regional Model Transfer (CRMT) provides a solution to crop classification challenges in target regions with limited labeled samples. However, when the source region (source domain) and the target region (target domain) are spatially distant, a
Externí odkaz:
https://doaj.org/article/d8c45e80026f46c1ad70e1c64406d917
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Fine-grained management of rice fields can enhance the yield and quality of rice crops. Challenges in achieving fine classification include interference from similar vegetation, the irregularity of natural field shapes, and complex scale var
Externí odkaz:
https://doaj.org/article/7a1a7e8040094c1d89b1f22047bafb0a
Publikováno v:
Geo-spatial Information Science, Pp 1-18 (2024)
Aiming at the issue of low accuracy in crop mapping and growth monitoring caused by imprecise calibration of radar time-series data, this paper proposed a Synthetic Aperture Radar (SAR) spatio-temporal error compensation method. By constructing a com
Externí odkaz:
https://doaj.org/article/0e11ac6e91324ddfb6188f85882381d1
Publikováno v:
Frontiers in Remote Sensing, Vol 5 (2024)
Remote sensing has enabled large-scale crop classification for understanding agricultural ecosystems and estimating production yields. In recent years, machine learning has become increasingly relevant for automated crop classification. However, the
Externí odkaz:
https://doaj.org/article/f013e094a96841428e05bad07a9381f4
Autor:
Hengbin Wang, Yu Yao, Zijing Ye, Wanqiu Chang, Junyi Liu, Yuanyuan Zhao, Shaoming Li, Zhe Liu, Xiaodong Zhang
Publikováno v:
GIScience & Remote Sensing, Vol 61, Iss 1 (2024)
Reliable classification results are crucial for guiding agricultural production, forecasting crop yield, and ensuring food security. Generating reliable classification results is relatively simple in regions with sufficient labeled samples, but regio
Externí odkaz:
https://doaj.org/article/0c5b024b6d6f4f76942a1d57b5f34513
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 134, Iss , Pp 104204- (2024)
Accurate crop mapping is critical for agricultural decisions and food security. Despite the widespread use of machine learning and deep learning in remote sensing for crop classification, mapping crops in mountainous smallholder farming systems remai
Externí odkaz:
https://doaj.org/article/0d269c644ec947728ba6c658746e2fe0
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 134, Iss , Pp 104172- (2024)
Information on the sowing areas and yields of crops is important for ensuring food security and reforming the agricultural modernization process, while crop classification and identification are core issues when attempting to acquire information on c
Externí odkaz:
https://doaj.org/article/87d19d80ae074b0d98f165d99a040ddf
Publikováno v:
Heliyon, Vol 10, Iss 22, Pp e36364- (2024)
Timely and accurate crop mapping plays an important role in food security, economic and environmental policies. Crop maps are also utilized for agro-environmental assessments and crop water usage monitoring. Because it provides periodic large-scale o
Externí odkaz:
https://doaj.org/article/3212e2725fd649b7b9ab6d401f8e9418
Autor:
Anil Antony, Ganesh Kumar R
Publikováno v:
e-Prime: Advances in Electrical Engineering, Electronics and Energy, Vol 9, Iss , Pp 100732- (2024)
Remote sensing images (RSIs), a keystone of modern agricultural technology, refer to spectral or visual data captured from drones, satellites, or aircraft without direct physical contact with the Earth's surface. These images provide a wide-ranging v
Externí odkaz:
https://doaj.org/article/7477b92d2b7c4c68b247353dcb0d4452