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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Meyer, L., Lemarchand, F., Sidiropoulos, P.,
A Deep Learning Architecture for Batch-mode Fully Automated Field Boundary Detection,
DOI Link 2012

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,
DOI Link 1904

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