{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:30:43Z","timestamp":1775665843449,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,8]],"date-time":"2020-11-08T00:00:00Z","timestamp":1604793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["4197071442"],"award-info":[{"award-number":["4197071442"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"International Programs &amp; Strategic Innovative Programs","award":["2017YFE0194900"],"award-info":[{"award-number":["2017YFE0194900"]}]},{"name":"Natural sciences Foundation of China","award":["41977154"],"award-info":[{"award-number":["41977154"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral images (HSIs), which obtain abundant spectral information for narrow spectral bands (no wider than 10 nm), have greatly improved our ability to qualitatively and quantitatively sense the Earth. Since HSIs are collected by high-resolution instruments over a very large number of wavelengths, the data generated by such sensors is enormous, and the amount of data continues to grow, HSI compression technique will play more crucial role in this trend. The classical method for HSI compression is through compression and reconstruction methods such as three-dimensional wavelet-based techniques or the principle component analysis (PCA) transform. In this paper, we provide an alternative approach for HSI compression via a generative neural network (GNN), which learns the probability distribution of the real data from a random latent code. This is achieved by defining a family of densities and finding the one minimizing the distance between this family and the real data distribution. Then, the well-trained neural network is a representation of the HSI, and the compression ratio is determined by the complexity of the GNN. Moreover, the latent code can be encrypted by embedding a digit with a random distribution, which makes the code confidential. Experimental examples are presented to demonstrate the potential of the GNN to solve image compression problems in the field of HSI. Compared with other algorithms, it has better performance at high compression ratio, and there is still much room left for improvements along with the fast development of deep-learning techniques.<\/jats:p>","DOI":"10.3390\/rs12213657","type":"journal-article","created":{"date-parts":[[2020,11,8]],"date-time":"2020-11-08T19:03:37Z","timestamp":1604862217000},"page":"3657","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Learning-Based Hyperspectral Imagery Compression through Generative Neural Networks"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3469-5624","authenticated-orcid":false,"given":"Chubo","family":"Deng","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Cen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3533-9966","authenticated-orcid":false,"given":"Lifu","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/978-81-322-2135-7_15","article-title":"Hyperspectral image compression algorithms\u2014A review","volume":"325","author":"Babu","year":"2015","journal-title":"Adv. Intell. Syst. Comput."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5765","DOI":"10.1109\/TGRS.2013.2292366","article-title":"Lossless to Lossy Dual-Tree BEZW Compression for Hyperspectral Images","volume":"52","author":"Babu","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1109\/LGRS.2009.2021674","article-title":"Lossy-to-Lossless Hyperspectral Image Compression Based on Multiplierless Reversible Integer TDLT\/KLT","volume":"6","author":"Wang","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2604","DOI":"10.1109\/TCYB.2019.2905793","article-title":"Dimensionality reduction of hyperspectral imagery based on spatial-spectral manifold learning","volume":"50","author":"Huang","year":"2019","journal-title":"IEEE Trans. Cybernetics"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2653","DOI":"10.1109\/36.803413","article-title":"Hyperspectral data analysis and supervised feature reduction via projection pursuit","volume":"37","author":"Jimenez","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2331","DOI":"10.1109\/TGRS.2002.804721","article-title":"Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction","volume":"40","author":"Bruce","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/MSP.2007.915001","article-title":"Compressive sampling and lossy compression","volume":"25","author":"Goyal","year":"2008","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1109\/LGRS.2010.2062484","article-title":"On the Impact of Lossy Compression on Hyperspectral Image Classification and Unmixing","volume":"8","author":"Zortea","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.dsp.2010.06.002","article-title":"3-D medical image compression using 3-D wavelet coders","volume":"21","author":"Sriraam","year":"2011","journal-title":"Digit. Signal Process."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Motta, G., Rizzo, F., and Storer, J.A. (2006). An architecture for the compression of hyperspectral imagery. Hyperspectral Image Compression, Springer. Chapter 1.","DOI":"10.1007\/0-387-28600-4"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.1109\/83.847830","article-title":"High performance scalable image compression with EBCOT","volume":"9","author":"Taubman","year":"2000","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chang, C.-I. (2007). 3D wavelet-based compression of hyperspectral imagery. Hyperspectral Data Exploitation: Theory and Applications, John Wiley & Sons, Inc.","DOI":"10.1002\/0470124628"},{"key":"ref_13","unstructured":"Rucker, J.T., Fowler, J.E., and Younan, N.H. (2005, January 25\u201329). JPEG2000 coding strategies for hyperspectral data. Proceedings of the International Geoscience and Remote Sensing Symposium, Seoul, South Korea."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1109\/LGRS.2005.859942","article-title":"Progressive 3-D coding of hyperspectral images based on JPEG 2000","volume":"3","author":"Penna","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Motta, G., Rizzo, F., and Storer, J.A. (2006). Compression of earth science data with JPEG2000. Hyperspectral Image Compression, Springer. Chapter 2.","DOI":"10.1007\/0-387-28600-4"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1109\/LGRS.2006.888109","article-title":"Hyperspectral Image Compression Using JPEG2000 and Principal Component Analysis","volume":"4","author":"Du","year":"2007","journal-title":"IEEE Geosci Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1109\/LGRS.2013.2247021","article-title":"A Wavelet Approach for Estimating Chlorophyll-A From Inland Waters With Reflectance Spectroscopy","volume":"11","author":"Ampe","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","unstructured":"Golub, G.H., Loan, C.F.V., and Jolliffe, I. (2002). Principal Component Analysis, Springer."},{"key":"ref_19","unstructured":"Masters, D., and Luschi, C. (2018). Revisiting small batch training for deep neural networks. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1366\/000370206778062093","article-title":"Fourier Transform Hyperspectral Visible Imaging and the Nondestructive Analysis of Potentially Fraudulent Documents","volume":"60","author":"Brauns","year":"2006","journal-title":"Appl. Spectrosc."},{"key":"ref_21","first-page":"20","article-title":"Impact of training set batch size on the performance of convolutional neural networks for diverse datasets","volume":"20","author":"Radiuk","year":"2017","journal-title":"Inf. Technol. Manag. Sci."},{"key":"ref_22","unstructured":"Zhang, L., Wei, W., Zhang, Y., Tian, C., and Li, F. (2015, January 7\u201312). Reweighted laplace prior based hyperspectral compressive sensing for unknown sparsity. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_23","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv."},{"key":"ref_24","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial networks. Proceedings of the Annual Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_25","unstructured":"Danfeng, H., Lianru, G., Jing, Y., Bing, Z., Antonio, P., and Jocelyn, C. (2020). Graph Convolutional Networks for Hyperspectral Image Classification. IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gray, R.M. (2011). Entropy and Information Theory, Springer Science & Business Media.","DOI":"10.1007\/978-1-4419-7970-4"},{"key":"ref_27","unstructured":"Simonyan, K., and Zisserman, A. (2014, September 04). Very deep convolutional networks for large-scale image recognition. Available online: https:\/\/arxiv.org\/abs\/1409.1556."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Blei, D.M., Kucukelbir, A., and Mcauliffe, J.D. (2017). Variational Inference: A Review for Statisticians. J. Am. Stat. Assoc., 112.","DOI":"10.1080\/01621459.2017.1285773"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.image.2003.10.003","article-title":"Downsampling dependent upsampling of images","volume":"19","author":"Frajka","year":"2004","journal-title":"Signal Process. Image Commun."},{"key":"ref_30","first-page":"10","article-title":"Probability distributions and maximum entropy","volume":"6","author":"Conrad","year":"2004","journal-title":"Entropy"},{"key":"ref_31","first-page":"15","article-title":"The notion of entropy of finite probabilistic schemes (Russian)","volume":"11","author":"Faddeev","year":"1956","journal-title":"Uspekhi Mat. Nauk."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1109\/72.317740","article-title":"Pruning recurrent neural networks for improved generalization performance","volume":"5","author":"Giles","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_33","first-page":"2177","article-title":"Neural Word Embedding as Implicit Matrix Factorization","volume":"3","author":"Levy","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional generative adversarial nets. arXiv."},{"key":"ref_35","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1109\/TGRS.2002.800280","article-title":"Anomaly detection and classification for hyperspectral imagery","volume":"40","author":"Chang","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/21\/3657\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:30:42Z","timestamp":1760178642000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/21\/3657"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,8]]},"references-count":36,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["rs12213657"],"URL":"https:\/\/doi.org\/10.3390\/rs12213657","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,8]]}}}