Satellite imagery retrieval based on adaptive Gaussian–Markov random field model with Bayes deep convolutional neural network
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geometry and Topology,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s00500-023-09418-9.pdf
Reference50 articles.
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2. Bouteldja S, Kourgli A, Aissa AB (2019) Efficient local-region approach for high-resolution remote-sensing image retrieval and classification. J Appl Remote Sens 13(1):016512
3. Chen M, Strobl J (2013) Multispectral textured image segmentation using a multi-resolution fuzzy Markov random field model on variable scales in the wavelet domain. Int J Remote Sens 34(13):4550–4569
4. Demir B, Bruzzone L (2014) A novel active learning method in relevance feedback for content-based remote sensing image retrieval. IEEE Trans Geosci Remote Sens 53(5):2323–2334
5. Dong Q, Luo AG (2020) Progress indication for deep learning model training: a feasibility demonstration. IEEE Access 8:79811–79843
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