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Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks
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. I...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321049/ https://www.ncbi.nlm.nih.gov/pubmed/34460726 http://dx.doi.org/10.3390/jimaging6040024 |
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author | Giannopoulos, Michalis Aidini, Anastasia Pentari, Anastasia Fotiadou, Konstantina Tsakalides, Panagiotis |
author_facet | Giannopoulos, Michalis Aidini, Anastasia Pentari, Anastasia Fotiadou, Konstantina Tsakalides, Panagiotis |
author_sort | Giannopoulos, Michalis |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8321049 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83210492021-08-26 Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks Giannopoulos, Michalis Aidini, Anastasia Pentari, Anastasia Fotiadou, Konstantina Tsakalides, Panagiotis J Imaging Article 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. MDPI 2020-04-18 /pmc/articles/PMC8321049/ /pubmed/34460726 http://dx.doi.org/10.3390/jimaging6040024 Text en © 2020 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Giannopoulos, Michalis Aidini, Anastasia Pentari, Anastasia Fotiadou, Konstantina Tsakalides, Panagiotis Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks |
title | Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks |
title_full | Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks |
title_fullStr | Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks |
title_full_unstemmed | Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks |
title_short | Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks |
title_sort | classification of compressed remote sensing multispectral images via convolutional neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321049/ https://www.ncbi.nlm.nih.gov/pubmed/34460726 http://dx.doi.org/10.3390/jimaging6040024 |
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