A Novel Depth-Wise Separable Convolutional Model for Remote Sensing Scene Classification
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12524-024-01904-3.pdf
Reference38 articles.
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3. Ansith, S., & Bini, A. A. (2022). Land use classification of high resolution remote sensing images using an encoder based modified GAN architecture. Displays, 74, 102229. https://doi.org/10.1016/j.displa.2022.102229
4. Basu, S., Ganguly, S., Mukhopadhyay, S., DiBiano, R., Karki, M., & Nemani, R. (2015). Deepsat: A learning framework for satellite imagery. In Proceedings of the 23rd SIGSPATIAL international conference on advances in geographic information systems. 1–10. https://doi.org/10.48550/arXiv.1509.03602
5. Bera, S., & Shrivastava, V. K. (2020). Analysis of various optimizers on deep convolutional neural network model in the application of hyperspectral remote sensing image classification. International Journal of Remote Sensing, 41(7), 2664–2683. https://doi.org/10.1080/01431161.2019.1694725
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