Crop Classification Based on Multispectral and Multitemporal Images Using CNN and GRU
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-5994-5_13
Reference15 articles.
1. Seydi ST, Amani M, Ghorbanian A (2022) A dual attention convolutional neural network for crop classification using time-series sentinel-2 imagery. Remote Sens 14:498 https://doi.org/10.3390/rs14030498
2. Wang L, Wang J, Liu Z, Zhu J, Qin F (2022) Evaluation of a deep-learning model for multispectral remote sensing of land use and crop classification. Crop J 2214–5141. https://doi.org/10.1016/j.cj.2022.01.009
3. Ji S, Zhang C, Xu A, Shi Y, Duan Y (2018) 3D convolutional neural networks for crop classification with multi-temporal remote sensing images. Remote Sens 10:75. https://doi.org/10.3390/rs10010075
4. Pott L, Amado T, Schwalbert R, Corassa G, Ciampitti I (2021) Satellite-based data fusion crop type classification and mapping in Rio Grande do Sul, Brazil. ISPRS J Photogrammetry Remote Sens 176:196–210 https://doi.org/10.1016/j.isprsjprs.2021.04.015
5. Wang Z, Zhao Z, Yin C (2022) Fine crop classification based on UAV hyperspectral images and random forest. ISPRS Int J Geo-Inf 11:252. https://doi.org/10.3390/ijgi11040252
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