Time-Weighted Dynamic Time Warping Classification Algorithm for Land Cover Mapping by Using SAR Imagery
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3191-6_28
Reference15 articles.
1. Moharana, S., Kambhammettu, B.V.N.P., Chintala, S., Rani, A.S. and Avtar, R.: Spatial distribution of inter- and intra-crop variability using time-weighted dynamic time warping analysis from Sentinel-1 datasets. Remote Sens. Appl. Soc. Environ. 24, 100630 (2021). https://doi.org/10.1016/j.rsase.2021.100630
2. Cheng, K., Wang, J.: Forest-type classification using time-weighted dynamic timewarping analysis in mountain areas: a case study in southern China. Forests 10(11), 1–18 (2019). https://doi.org/10.3390/f10111040
3. Csillik, O., Belgiu, M., Asner, G.P., Kelly, M.: Object-based time-constrained dynamic time warping classification of crops using Sentinel-2. Remote Sens. 11(10) (2019). https://doi.org/10.3390/rs11101257.
4. Morell-Monzó, S., Estornell, J., Sebastiá-Frasquet, M.T.: Assessing the capabilities of high-resolution spectral, altimetric, and textural descriptors for mapping the status of citrus parcels. Comput. Electron. Agric. 204(746) (2023). https://doi.org/10.1016/j.compag.2022.107504
5. Saini, R., Ghosh, S.K.: Crop classification on single date Sentinel-2 ımagery using random forest and suppor vector machine. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 43–45(November), 683–688 (2018). https://doi.org/10.5194/isprs-archives-xlii-5-683-2018
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