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The development of learning-based hyperspectral image compression methods has recently attracted great attention in remote sensing.
S. Lim, K. Sohn, and C. Lee, “Compression for hyperspectral images using three dimensional wavelet transform,” IEEE International Geoscience and Remote Sensing Symposium , pp. 109–111, 2001
2001
Earlier work this paper cites.
D. Landgrebe, “Hyperspectral image data analysis,” IEEE Signal Processing Magazine , vol. 19, no. 1, pp. 17–28, 2002
2002
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B. Penna, T. Tillo, E. Magli, and G. Olmo, “A new low complexity klt for lossy hyperspectral data compression,” IEEE International Symposium on Geoscience and Remote Sensing , pp. 3525–3528, 2006
2006
Earlier work this paper cites.
Q. Du and J. E. Fowler, “Hyperspectral image compression using jpeg2000 and principal component analysis,” IEEE Geoscience and Remote Sensing Letters , vol. 4, no. 2, pp. 201–205, 2007
2007
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2014
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L. Guanter, H. Kaufmann, K. Segl, S. Foerster, C. Rogass, S. Chabrillat, T. Kuester, A. Hollstein, G. Rossner, C. Chlebek et al. , “The enmap spaceborne imaging spectroscopy mission for earth observation,” Remote Sensing , vol. 7, no. 7, pp. 8830–8857, 2015
2015
Cited alongside, same era.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” International Conference on Machine Learning , pp. 448–456, 2015
2015
Cited alongside, same era.
Y. Dua, V. Kumar, and R. S. Singh, “Comprehensive review of hyperspectral image compression algorithms,” Optical Engineering , vol. 59, no. 9, pp. 090 902–090 902, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Y. Chong, L. Chen, and S. Pan, “End-to-end joint spectral–spatial compression and reconstruction of hyperspectral images using a 3d convolutional autoencoder,” Journal of Electronic Imaging , vol. 30, no. 4, p. 041403, 2021
2021
Later among the works it cites.
J. Kuester, W. Gross, S. Schreiner, M. Heizmann, and W. Middelmann, “Transferability of convolutional autoencoder model for lossy compression to unknown hyperspectral prisma data,” IEEE Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing , pp. 1–5, 2022
2022
Later among the works it cites.
J. Kuester, J. Anastasiadis, W. Middelmann, and M. Heizmann, “Investigating the influence of hyperspectral data compression on spectral unmixing,” SPIE Image and Signal Processing for Remote Sensing , vol. 12267, pp. 125–135, 2022
2022
Later among the works it cites.
R. La Grassa, C. Re, G. Cremonese, and I. Gallo, “Hyperspectral data compression using fully convolutional autoencoder,” Remote Sensing , vol. 14, no. 10, p. 2472, 2022
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J. Kuester, W. Gross, and W. Middelmann, “1d-convolutional autoencoder based hyperspectral data compression,” International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences , vol. 43, pp. 15–21, 2021
2021
Cited alongside, same era.
2022
Later among the works it cites.