Deep learning technique for image satellite processing
DOI:
https://doi.org/10.58190/imiens.2023.9Keywords:
Convolutional neural network, Satellite Images, Protocol, Remote SensingAbstract
Recently, several technological solutions based on deep learning have been developed for the processing of multispectral satellite images. These solutions have broadened the scope of spatial remote sensing and further explored the earth and space by avoiding human efforts in heavy manual tasks. However, even though this progress gives even better performance it is difficult to design an efficient deep-learning pipeline for satellite image processing in the absence of a well-developed standard. Thus, we propose a new framework to design and implement a machine-learning model for satellite image processing through a set of machine-learning methods in the form of a protocol adapted to the context of space imagery. The choice of the dataset adapted metrics, exploitation of the product format, data enrichment, spatiotemporal indexing and analysis, contrast enhancement and noise reduction/suppression, network parameterization, model scalability, data normalization, spectral dependency, and processing complexity reduction are among the methods adapted to the processing of multispectral satellite images by deep learning. All these methods are analyzed in depth in order to understand their usefulness and their contribution to the performance of learning models before testing them on real use cases. This method constitutes the first standard framework for the use of deep learning in the processing of multispectral satellite images.
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Zhang, Bing, et al. "Progress and challenges in intelligent remote sensing satellite systems." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2022).
Chen, Jinyue, et al. "Remote sensing big data for water environment monitoring: current status, challenges, and future prospects." Earth's Future 10.2 (2022): e2021EF002289.
R. Pal, S. Mukhopadhyay, D. Chakraborty, and P. N. Suganthan, “Very high-resolution satellite image segmentation using variable-length multi-objective genetic clustering for multi-class change detection,” Journal of King Saud University - Computer and Information Sciences, 2022.
M. U. Müller, N. Ekhtiari, R. M. Almeida, and C. Rieke, “Super-resolution of multispectral satellite images using Convolutional Neural Networks,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. V-1-2020, pp. 33–40, 2020.
S. Aati, C. Milliner, and J.-P. Avouac, “A new approach for 2-D and 3-D precise measurements of ground deformation from optimized registration and correlation of optical images and ICA-based filtering of image geometry artifacts,” Remote Sensing of Environment, vol. 277, p. 113038, 2022.
W. Li, C. Yang, Y. Peng, and J. Du, “A pseudo-siamese deep convolutional neural network for Spatiotemporal Satellite Image Fusion,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 1205–1220, 2022.
R. Bharti, D. Saini, and R. Malik, “A novel approach of transfer learning for Satellite Image Classification,” Proceedings of Data Analytics and Management, pp. 379–392, 2022.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, ‘ImageNet Classification with Deep Convolutional Neural Networks’, in Advances in Neural Information Processing Systems, 2012, vol. 25, pp. 1097–1105.
I. J. Goodfellow et al., ‘Generative Adversarial Networks’, arXiv, arXiv:1406.2661, Jun. 2014. doi: 10.48550/arXiv.1406.2661.
D. P. Kingma and M. Welling, “An introduction to variational autoencoders,” Foundations and Trends® in Machine Learning, vol. 12, no. 4, pp. 307–392, 2019.
I. Sutskever, J. Martens, and G. Hinton, ‘Generating Text with Recurrent Neural Networks’, in Proceedings of the 28th International Conference on Machine Learning (ICML-11), New York, NY, USA, Jun. 2011, pp. 1017–1024.
Milleville, Kenzo, Steven Verstockt, and Nico Van de Weghe. "Automatic Georeferencing of Topographic Raster Maps." ISPRS International Journal of Geo-Information 11.7 (2022): 387.
M. Claverie, J. Ju, J. G. Masek, J. L. Dungan, E. F. Vermote, J.-C. Roger, S. V. Skakun, and C. Justice, “The Harmonized Landsat and sentinel-2 surface reflectance data set,” Remote Sensing of Environment, vol. 219, pp. 145–161, 2018.
V. P. Lalitha, S. Rangaswamy, C. R. Gouthami, T. Jai Balaj, P. Kumar, and R. G. Dolli, “Satellite Image Enhancement Using Neural Networks,” 2018 3rd International Conference on Inventive Computation Technologies (ICICT), 2018.
B. E. Ewing, S. D. Butler, and M. A. Marciniak, “Improved grazing angle bidirectional reflectance distribution function model using Rayleigh–rice polarization factor and adaptive microfacet distribution function,” Optical Engineering, vol. 57, no. 10, p. 1, 2018.
‘Atmospheric Compensation | Satellite Imaging Corp’. https://www.satimagingcorp.com/services/atmospheric-compensation/ (accessed May 29, 2022).
Y. Wu, C. R. Duguay, and L. Xu, “Assessment of Machine Learning Classifiers for Global Lake Ice cover mapping from Modis Toa reflectance data,” Remote Sensing of Environment, vol. 253, p. 112206, 2021.
‘Memoire Online - Contribution à l’optimisation complexe par des techniques de swarm intelligence - Lamia Benameur’. https://www.memoireonline.com/10/12/6363/Contribution--loptimisation-complexe-par-des-techniques-de-swarm-intelligence.html (accessed May 29, 2022).
I. J. Goodfellow et al., ‘Generative Adversarial Networks’, arXiv, arXiv:1406.2661, Jun. 2014. doi: 10.48550/arXiv.1406.2661.
F. Movahedi, J. L. Coyle, and E. Sejdic, “Deep belief networks for electroencephalography: A review of recent contributions and future outlooks,” IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 3, pp. 642–652, 2018.
D. P. Kingma and M. Welling, “An introduction to variational autoencoders,” Foundations and Trends® in Machine Learning, vol. 12, no. 4, pp. 307–392, 2019.
I. Sutskever, J. Martens, and G. Hinton, ‘Generating Text with Recurrent Neural Networks’, in Proceedings of the 28th International Conference on Machine Learning (ICML-11), New York, NY, USA, Jun. 2011, pp. 1017–1024.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, ‘ImageNet Classification with Deep Convolutional Neural Networks’, in Advances in Neural Information Processing Systems, 2012, vol. 25, pp. 1097–1105.
S. Aati, C. Milliner, and J.-P. Avouac, “A new approach for 2-D and 3-D precise measurements of ground deformation from optimized registration and correlation of optical images and ICA-based filtering of image geometry artifacts,” Remote Sensing of Environment, vol. 277, p. 113038, 2022.
W. Li, C. Yang, Y. Peng, and J. Du, “A pseudo-siamese deep convolutional neural network for Spatiotemporal Satellite Image Fusion,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 1205–1220, 2022.
R. Pal, S. Mukhopadhyay, D. Chakraborty, and P. N. Suganthan, “Very high-resolution satellite image segmentation using variable-length multi-objective genetic clustering for multi-class change detection,” Journal of King Saud University - Computer and Information Sciences, 2022.
L. Ma, Y. Liu, X. Zhang, Y. Ye, G. Yin, and B. A. Johnson, “Deep learning in remote sensing applications: A Meta-analysis and Review,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 152, pp. 166–177, 2019.
W. Yi, Y. Wang, Y. Zeng, Y. Wang, and J. Xu, “Comprehensive evaluation of the GF-4 satellite image quality from 2015 to 2020,” ISPRS International Journal of Geo-Information, vol. 10, no. 6, p. 406, 2021.
M. A. Trujillo-Jiménez, A. L. Liberoff, N. Pessacg, C. Pacheco, L. Díaz, and S. Flaherty, “Satred: New classification land use/land cover model based on multi-spectral satellite images and neural networks applied to a semiarid valley of Patagonia,” Remote Sensing Applications: Society and Environment, vol. 26, p. 100703, 2022.
Liping Di, Yuanzheng Shao, and Lingjun Kang, “Implementation of Geospatial Data Provenance in a web service workflow environment with ISO 19115 and ISO 19115-2 Lineage Model,” IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 11, pp. 5082–5089, 2013.
A. Mashhoori, J. W. Bengtson, D. E. Chalmers, C. Xian, and C. R. Bryant, ‘Masquage D’ombre Et De Nuage Pour Des Applications Agricoles Utilisant Des Réseaux Neuronaux Convolutionnels’, Aug. 13, 2020 Accessed: May 29, 2022. [Online]. Available: https://patentscope.wipo.int/search/fr/detail.jsf?docId=WO2020160643
S. Santurkar, D. Tsipras, A. Ilyas, and A. Madry, “How Does Batch Normalization Help Optimization?,” Advances in Neural Information Processing Systems, vol. 31, 2018.
Soufi, O., Aarab, Z., & Belouadha, F. Z. (2022, March). Benchmark of deep learning models for single image super-resolution (SISR). In 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) (pp. 1-8). IEEE.
Q. Zhang, M. Zhang, T. Chen, Z. Sun, Y. Ma, and B. Yu, “Recent advances in convolutional neural network acceleration,” Neurocomputing, vol. 323, pp. 37–51, 2019.
T. Lu, J. Wang, Y. Zhang, Z. Wang, and J. Jiang, “Satellite image super-resolution via multi-scale residual deep neural network,” Remote Sensing, vol. 11, no. 13, p. 1588, 2019.
A. Sharma and A. Khunteta, “Satellite image enhancement using discrete wavelet transform, singular value decomposition and its noise performance analysis,” 2016 International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE), 2016.
V. Singh, G. Kumar and G. Arora, "Analytical evaluation for the enhancement of satellite images using swarm intelligence techniques," 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), 2016, pp. 2401-2405.
V. V. Naik and S. Gharge, “Satellite image resolution enhancement using DTCWT and DTCWT based fusion,” 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2016.
B. Settles, ‘Active Learning Literature Survey’, University of Wisconsin-Madison Department of Computer Sciences, Technical Report, 2009. Accessed: May 29, 2022. [Online]. Available: https://minds.wisconsin.edu/handle/1793/60660.
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