22.2.2.2.1 Agricultural Field Extraction

Chapter Contents (Back)
Agricultural Field. Field Extraction. Region-Based.
See also Irrigation Monitoring, Irrigated Field Detection, Land Use Analysis.
See also Rice Crop Analysis, Production, Detection, Health, Change.

Yan, F.Q.[Feng-Qin], Yu, L.X.[Ling-Xue], Yang, C.B.[Chao-Bin], Zhang, S.[Shuwen],
Paddy Field Expansion and Aggregation Since the Mid-1950s in a Cold Region and Its Possible Causes,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Wagner, M.P.[Matthias P.], Oppelt, N.[Natascha],
Deep Learning and Adaptive Graph-Based Growing Contours for Agricultural Field Extraction,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Vlachopoulos, O.[Odysseas], Leblon, B.[Brigitte], Wang, J.F.[Jin-Fei], Haddadi, A.[Ataollah], LaRocque, A.[Armand], Patterson, G.[Greg],
Delineation of Crop Field Areas and Boundaries from UAS Imagery Using PBIA and GEOBIA with Random Forest Classification,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Liu, J.[Jin], Zheng, H.[Haokun],
EFN: Field-Based Object Detection for Aerial Images,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Chang, L.[Lena], Chen, Y.T.[Yi-Ting], Wang, J.H.[Jung-Hua], Chang, Y.L.[Yang-Lang],
Rice-Field Mapping with Sentinel-1A SAR Time-Series Data,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Gilcher, M.[Mario], Udelhoven, T.[Thomas],
Field Geometry and the Spatial and Temporal Generalization of Crop Classification Algorithms: A Randomized Approach to Compare Pixel Based and Convolution Based Methods,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Taravat, A.[Alireza], Wagner, M.P.[Matthias P.], Bonifacio, R.[Rogerio], Petit, D.[David],
Advanced Fully Convolutional Networks for Agricultural Field Boundary Detection,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Waldner, F.[François], Diakogiannis, F.I.[Foivos I.], Batchelor, K.[Kathryn], Ciccotosto-Camp, M.[Michael], Cooper-Williams, E.[Elizabeth], Herrmann, C.[Chris], Mata, G.[Gonzalo], Toovey, A.[Andrew],
Detect, Consolidate, Delineate: Scalable Mapping of Field Boundaries Using Satellite Images,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
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Wen, C.[Caiyun], Lu, M.[Miao], Bi, Y.[Ying], Zhang, S.N.[Sheng-Nan], Xue, B.[Bing], Zhang, M.J.[Meng-Jie], Zhou, Q.[Qingbo], Wu, W.B.[Wen-Bin],
An Object-Based Genetic Programming Approach for Cropland Field Extraction,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
BibRef

Li, T.[Ting], Johansen, K.[Kasper], McCabe, M.F.[Matthew F.],
A machine learning approach for identifying and delineating agricultural fields and their multi-temporal dynamics using three decades of Landsat data,
PandRS(186), 2022, pp. 83-101.
Elsevier DOI 2203
Center-pivot field, Delineation, DBSCAN, Convolution neural networks, Spectral clustering, Random forest BibRef

Lu, R.[Rui], Wang, N.[Nan], Zhang, Y.B.[Yan-Bin], Lin, Y.N.[Ye-Neng], Wu, W.Q.[Wen-Qiang], Shi, Z.[Zhou],
Extraction of Agricultural Fields via DASFNet with Dual Attention Mechanism and Multi-scale Feature Fusion in South Xinjiang, China,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Mei, W.Y.[Wei-Ye], Wang, H.Y.[Hao-Yu], Fouhey, D.[David], Zhou, W.Q.[Wei-Qi], Hinks, I.[Isabella], Gray, J.M.[Josh M.], van Berkel, D.[Derek], Jain, M.[Meha],
Using Deep Learning and Very-High-Resolution Imagery to Map Smallholder Field Boundaries,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Wang, S.[Sherrie], Waldner, F.[François], Lobell, D.B.[David B.],
Unlocking Large-Scale Crop Field Delineation in Smallholder Farming Systems with Transfer Learning and Weak Supervision,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
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Zhong, B.[Bo], Wei, T.F.[Teng-Fei], Luo, X.B.[Xiao-Bo], Du, B.[Bailin], Hu, L.F.[Long-Fei], Ao, K.[Kai], Yang, A.[Aixia], Wu, J.J.[Jun-Jun],
Multi-Swin Mask Transformer for Instance Segmentation of Agricultural Field Extraction,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
BibRef

Ge, J.[Ji], Zhang, H.[Hong], Xu, L.[Lu], Sun, C.L.[Chun-Ling], Duan, H.[Haoxuan], Guo, Z.[Zihuan], Wang, C.[Chao],
A Physically Interpretable Rice Field Extraction Model for PolSAR Imagery,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
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Liu, X.C.[Xiang-Chen], Shao, Y.[Yun], Li, K.[Kun], Liu, Z.[Zhiqu], Liu, L.[Long], Xiao, X.[Xiulai],
Backscattering Statistics of Indoor Full-Polarization Scatterometric and Synthetic Aperture Radar Measurements of a Rice Field,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
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Meyer, L., Lemarchand, F., Sidiropoulos, P.,
A Deep Learning Architecture for Batch-mode Fully Automated Field Boundary Detection,
ISPRS20(B3:1009-1016).
DOI Link 2012
BibRef

Wakabayashi, H., Motohashi, K., Kitagami, T., Tjahjono, B., Dewayani, S., Hidayat, D., Hongo, C.,
Flooded Area Extraction of Rice Paddy Field in Indonesia Using Sentinel-1 Sar Data,
Environmental19(73-76).
DOI Link 1904
BibRef

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Subpixel Target, Subpixel Land Use, Tiny Objects .


Last update:May 22, 2023 at 22:32:27