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Article

Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks

1
Signal Processing Lab (SPL), Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 70013 Crete, Greece
2
Computer Science Department, University of Crete, 70013 Crete, Greece
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
J. Imaging 2020, 6(4), 24; https://doi.org/10.3390/jimaging6040024
Received: 17 January 2020 / Revised: 27 March 2020 / Accepted: 15 April 2020 / Published: 18 April 2020
(This article belongs to the Special Issue Multispectral Imaging)
Multispectral sensors constitute a core Earth observation image technology generating massive high-dimensional observations. To address the communication and storage constraints of remote sensing platforms, lossy data compression becomes necessary, but it unavoidably introduces unwanted artifacts. In this work, we consider the encoding of multispectral observations into high-order tensor structures which can naturally capture multi-dimensional dependencies and correlations, and we propose a resource-efficient compression scheme based on quantized low-rank tensor completion. The proposed method is also applicable to the case of missing observations due to environmental conditions, such as cloud cover. To quantify the performance of compression, we consider both typical image quality metrics as well as the impact on state-of-the-art deep learning-based land-cover classification schemes. Experimental analysis on observations from the ESA Sentinel-2 satellite reveals that even minimal compression can have negative effects on classification performance which can be efficiently addressed by our proposed recovery scheme. View Full-Text
Keywords: multispectral image classification; deep learning; convolutional neural networks; residual learning; compression; quantization; tensor unfoldings; nuclear norm multispectral image classification; deep learning; convolutional neural networks; residual learning; compression; quantization; tensor unfoldings; nuclear norm
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MDPI and ACS Style

Giannopoulos, M.; Aidini, A.; Pentari, A.; Fotiadou, K.; Tsakalides, P. Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks. J. Imaging 2020, 6, 24. https://doi.org/10.3390/jimaging6040024

AMA Style

Giannopoulos M, Aidini A, Pentari A, Fotiadou K, Tsakalides P. Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks. Journal of Imaging. 2020; 6(4):24. https://doi.org/10.3390/jimaging6040024

Chicago/Turabian Style

Giannopoulos, Michalis, Anastasia Aidini, Anastasia Pentari, Konstantina Fotiadou, and Panagiotis Tsakalides. 2020. "Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks" Journal of Imaging 6, no. 4: 24. https://doi.org/10.3390/jimaging6040024

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