Accurate classification of land use and land cover using a boundary-specific two-level learning approach augmented with auxiliary features in Google Earth Engine

Author:

Selvaraj Rohini,Amali D Geraldine Bessie

Publisher

Springer Science and Business Media LLC

Subject

Management, Monitoring, Policy and Law,Pollution,General Environmental Science,General Medicine

Reference60 articles.

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2. Acharya, T. D. & Yang, I. (2015). Exploring landsat 8. International journal of IT, Engineering and Applied Sciences Research, 4(4), 8. https://www.researchgate.net/profile/Tri-Acharya/publication/311901147_Exploring_Landsat_8/links/589c0de6458515e5f4549e58/Exploring-Landsat-8.pdf. Accessed 5 Oct 2023

3. Aggarwal, N., Srivastava, M., & Dutta, M. (2016). Comparative analysis of pixel-based and object-based classification of high resolution remote sensing images – A review. International Journal of Engineering Trends and Technology, 38(1), 5–11. https://doi.org/10.14445/22315381/ijett-v38p202

4. Al-Abdulrazzak, D., & Pauly, D. (2014). Ground-truthing the ground-truth: reply to Garibaldi et al.’s comment on “Managing fisheries from space: Google Earth improves estimates of distant fish catches.” ICES Journal of Marine Science, 71(7), 1927–1931. https://doi.org/10.1038/278097a0

5. Alaguraja, P., Durairaju, S., Yuvaraj, D., Sekar, M., Muthuveerran, P., Manivel, M., & Thirunavukkarasu, A. (2010). Land use and land cover mapping – Madurai District, Tamilnadu, India using remote sensing and GIS techniques. International Journal of Civil and Strucutral Engineering, 1(1), 91–100.

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