Abstract
The best machine learning strategy is deep neural learning. Regardless of information wellsprings, current classification and remote sensing strategies are quickly presented. At that point, basic databases and run deep neural learning models are introduced, with deep belief network, convolutional neural network and stacked autoencoder. Besides, ideal design of such strategies for deep neural learning is abridged by Kappa coefficient and general exactness At long last, the present work along with future scope of satellite sensing deep neural learning classification has been provided. The present work explores the deep neural learning, which is guaranteed to be an overwhelming strategy for sensing classification of remote sensing.
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Change history
20 December 2019
The book was inadvertently published with the author’s name in Chapter 29 as Bharat Sharma, Bhuvidha Singh Tomar, Chander Bhuvan, Sumit Bhardwaj and Prakash Kumar whereas it should be Ranjana Thalore, Raunak Monir, Jeetu Sharma, Vikas Raina, P. P. Bhattacharya and V. K. Jain.
In addition to this, the Author’s name Dr. Deepak Sinwar, whose name has been now removed in Chapter 76. The erratum chapter has been updated with the change.
15 March 2022
The Editors have retracted this Chapter because it contains significant overlap with a previously published article by different authors [1]. All authors agree to this retraction.
[1] C. Yao, X. Luo, Y. Zhao, W. Zeng and X. Chen, “A review on image classification of remote sensing using deep learning,” 2017 3rd IEEE International Conference on Computer and Communications (ICCC), 2017, pp. 1947–1955, doi: 10.1109/CompComm.2017.8322878.
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Sharma, M.K., Verma, H. (2020). RETRACTED CHAPTER: Remote Sensing Classification Under Deep Learning: A Review. In: Somani, A.K., Shekhawat, R.S., Mundra, A., Srivastava, S., Verma, V.K. (eds) Smart Systems and IoT: Innovations in Computing. Smart Innovation, Systems and Technologies, vol 141. Springer, Singapore. https://doi.org/10.1007/978-981-13-8406-6_76
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DOI: https://doi.org/10.1007/978-981-13-8406-6_76
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