Rough Set, ELM Classifier and Deep Architecture for Remote Sensing Images
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-65392-6_5
Reference18 articles.
1. Thenkabail, P.S.: Remotely Sensed Data Characterization, Classification, and Accuracies, vol. 1. CRC Press, October 2015
2. Brandt, T., Mather, P.M.: Classification Methods for Remotely Sensed Data, Second edn. CRC Press, April 2016
3. Zou, Q., Ni, L., Zhang, T., Wang, Q.: Deep learning based feature selection for remote sensing scene classification. IEEE Geosci. Remote Sens. Lett. 12(11), 2321–2325 (2015). https://doi.org/10.1109/LGRS.2015.2475299
4. Mehmood, M., Shahzad, A., Zafar, B., Shabbir, A., Ali, N.: Remote sensing image classification: a comprehensive review and applications. Math. Probl. Eng. Eng. (2022). https://doi.org/10.1155/2022/5880959
5. Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
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