Crop type detection using an object-based classification method and multi-temporal Landsat satellite images
Author:
Publisher
Springer Science and Business Media LLC
Subject
Water Science and Technology,Agronomy and Crop Science,Environmental Engineering
Link
https://link.springer.com/content/pdf/10.1007/s10333-022-00901-x.pdf
Reference62 articles.
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2. Asgarian A, Soffianian A, Pourmanafi S (2016) Crop type mapping in a highly fragmented and heterogeneous agricultural landscape: a case of central Iran using multi-temporal Landsat 8 imagery. Comput Electron Agric 127:531–540
3. Baatz M (2000) Multiresolution Segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation, Angewandte Geographische Informationsverarbeitung pp. 12–23
4. Belgiu M, Csillik O (2018) Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis. Remote Sens Environ 204:509–23. https://doi.org/10.1016/j.rse.2017.10.005
5. Benvenuti F, Weill M (2010) Relationship between multi-spectral data and sugarcane crop yield. In: Proceedings of the 19th World Congress of Soil Science and Soil Solutions for a Changing World pp. 33–36
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