{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T08:52:07Z","timestamp":1765356727520,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,5,28]],"date-time":"2020-05-28T00:00:00Z","timestamp":1590624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>To achieve efficient lossless compression of hyperspectral images, we design a concatenated neural network, which is capable of extracting both spatial and spectral correlations for accurate pixel value prediction. Unlike conventional neural network based methods in the literature, the proposed neural network functions as an adaptive filter, thereby eliminating the need for pre-training using decompressed data. To meet the demand for low-complexity onboard processing, we use a shallow network with only two hidden layers for efficient feature extraction and predictive filtering. Extensive simulations on commonly used hyperspectral datasets and the standard CCSDS test datasets show that the proposed approach attains significant improvements over several other state-of-the-art methods, including standard compressors such as ESA, CCSDS-122, and CCSDS-123.<\/jats:p>","DOI":"10.3390\/jimaging6060038","type":"journal-article","created":{"date-parts":[[2020,5,28]],"date-time":"2020-05-28T12:36:58Z","timestamp":1590669418000},"page":"38","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Spatially and Spectrally Concatenated Neural Networks for Efficient Lossless Compression of Hyperspectral Imagery"],"prefix":"10.3390","volume":"6","author":[{"given":"Zhuocheng","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL 35899, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7265-2188","authenticated-orcid":false,"given":"W. David","family":"Pan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL 35899, USA"}]},{"given":"Hongda","family":"Shen","sequence":"additional","affiliation":[{"name":"Chubbs Insurance Inc., New York, NY 10020, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1109\/83.495955","article-title":"Lossless compression of AVIRIS images","volume":"5","author":"Roger","year":"1996","journal-title":"IEEE Trans. Image Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1109\/83.855427","article-title":"The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG-LS","volume":"9","author":"Weinberger","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1109\/LGRS.2009.2032370","article-title":"JPEG 2000 encoding of remote sensing multispectral images with no-data regions","volume":"7","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1109\/83.846242","article-title":"Context-based lossless interband compression - extending CALIC","volume":"9","author":"Wu","year":"2000","journal-title":"IEEE Trans. Image Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/LGRS.2003.822312","article-title":"Optimized onboard lossless and near-lossless compression of hyperspectral data using CALIC","volume":"1","author":"Magli","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/LSP.2005.862604","article-title":"Lossless compression of hyperspectral images using lookup tables","volume":"13","author":"Mielikainen","year":"2006","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1109\/LGRS.2008.917598","article-title":"Lossless compression of hyperspectral images using a quantized index to lookup tables","volume":"5","author":"Mielikainen","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5616","DOI":"10.1109\/TGRS.2016.2569485","article-title":"Regression Wavelet Analysis for Lossless Coding of Remote-Sensing Data","volume":"54","author":"Amrani","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1109\/LGRS.2018.2850938","article-title":"Multilevel Split Regression Wavelet Analysis for Lossless Compression of Remote Sensing Data","volume":"15","author":"Cortes","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"unstructured":"Klimesh, M. (2005). Low-Complexity Lossless Compression of Hyperspectral Imagery via Adaptive Filtering, The Interplanetary Network Progress Report.","key":"ref_10"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1109\/LSP.2004.840907","article-title":"Low-Complexity lossless compression of hyperspectral imagery via linear prediction","volume":"12","author":"Rizzo","year":"2005","journal-title":"IEEE Signal Process. Lett."},{"unstructured":"CCSDS (2019, February 01). Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression CCSDS 123.0-B-2, ser. Blue Book, February 2019. Available online: https:\/\/public.ccsds.org\/Pubs\/123x0b2c1.pdf.","key":"ref_12"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1109\/LGRS.2010.2041630","article-title":"An efficient lossless compression scheme for hyperspectral images using two-stage prediction","volume":"7","author":"Lin","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1109\/TGRS.2008.2009316","article-title":"Multiband lossless compression of hyperspectral images","volume":"47","author":"Magli","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Shen, H., and Pan, W.D. (2016, January 25\u201328). Predictive lossless compression of regions of interest in hyperspectral image via maximum correntropy criterion based least mean square learning. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","key":"ref_15","DOI":"10.1109\/ICIP.2016.7532745"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4187","DOI":"10.1109\/TGRS.2007.906085","article-title":"Lossless hyperspectral-image compression using context-based conditional average","volume":"45","author":"Wang","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2672","DOI":"10.1109\/TGRS.2009.2015291","article-title":"Exploiting calibration-induced artifacts in lossless compression of hyperspectral imagery","volume":"47","author":"Kiely","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2943","DOI":"10.1109\/TGRS.2003.820885","article-title":"Clustered DPCM for the lossless compression of hyperspectral images","volume":"41","author":"Mielikainen","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1109\/LGRS.2012.2191531","article-title":"Lossless compression of hyperspectral images using clustered linear prediction with adaptive prediction length","volume":"9","author":"Mielikainen","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1109\/LGRS.2007.900695","article-title":"Crisp and fuzzy adaptive spectral prediction for lossless and near-lossless compression of hyperspectral imagery","volume":"4","author":"Aiazzi","year":"2007","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","first-page":"2257","article-title":"HyperReconNet: Joint Coded Aperture Optimization and Image Reconstruction for Compressive Hyperspectral Imaging","volume":"28","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3130800.3130810","article-title":"High-quality hyperspectral reconstruction using a spectral prior","volume":"36","author":"Choi","year":"2017","journal-title":"ACM Trans. Graph."},{"doi-asserted-by":"crossref","unstructured":"Signoroni, A., Savardi, M., Baronio, A., and Benini, S. (2019). Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review. J. Imaging, 5.","key":"ref_23","DOI":"10.3390\/jimaging5050052"},{"doi-asserted-by":"crossref","unstructured":"Haut, J.M., Gallardo, J.A., Paoletti, M.E., Cavallaro, G., Plaza, J., Plaza, A., and Riedel, M. (2019). Cloud Deep Networks for Hyperspectral Image Analysis. IEEE Trans. Geosci. Remote Sens.","key":"ref_24","DOI":"10.1109\/TGRS.2019.2929731"},{"doi-asserted-by":"crossref","unstructured":"Valsesia, D., and Magli, E. (2019). High-Throughput Onboard Hyperspectral Image Compression With Ground-Based CNN Reconstruction. IEEE Trans. Geosci. Remote Sens.","key":"ref_25","DOI":"10.1109\/TGRS.2019.2927434"},{"doi-asserted-by":"crossref","unstructured":"Kumar, S., Chaudhuri, S., Banerjee, B., and Ali, F. (2018, January 8\u201314). Onboard hyperspectral image compression using compressed sensing and deep learning. Proceedings of the 2018 IEEE European Conference on Computer Vision (ECCV), Munich, Germany.","key":"ref_26","DOI":"10.1007\/978-3-030-11012-3_3"},{"unstructured":"Dusselaar, R., and Paul, M. (2012). A block-based inter-band predictor using multilayer propagation neural network for hyperspectral image compression. arXiv.","key":"ref_27"},{"doi-asserted-by":"crossref","unstructured":"Jiang, Z., Pan, W.D., and Shen, H. (2018, January 11\u201313). LSTM Based Adaptive Filtering for Reduced Prediction Errors of Hyperspectral Images. Proceedings of the 2018 6th IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE 2018), Huntsville, AL, USA.","key":"ref_28","DOI":"10.1109\/WiSEE.2018.8637354"},{"doi-asserted-by":"crossref","unstructured":"Shen, H., Jiang, Z., and Pan, W.D. (2018). Efficient Lossless Compression of Multitemporal Hyperspectral Image Data. J. Imaging, 4.","key":"ref_29","DOI":"10.3390\/jimaging4120142"},{"unstructured":"Cover, T.M., and Thomas, J.A. (2006). Elements of Information Theory, Wiley-Interscience. [2nd ed.].","key":"ref_30"},{"unstructured":"Zeiler, M.D. (2012). Adadelta: An adaptive learning rate method. arXiv.","key":"ref_31"},{"unstructured":"CSNN (2020, May 08). The Code of CSNN Structure. Available online: https:\/\/github.com\/jj574435561\/imagecomCSNN.","key":"ref_32"},{"unstructured":"CCSDS (2017, December 01). Lossless Multispectral & Hyperspectral Image Compression CCSDS 123.0-B-1, ser. Blue Book, May 2012. Available online: https:\/\/public.ccsds.org\/Pubs\/123x0b1ec1.pdf.","key":"ref_33"},{"unstructured":"Rice, R.F. (1979). Some Practical Universal Noiseless Coding Techniques.","key":"ref_34"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1145\/214762.214771","article-title":"Arithmetic coding for data compression","volume":"30","author":"Witten","year":"1987","journal-title":"Commun. ACM"},{"unstructured":"GIC (2014, August 30). Hyperspectral Remote Sensing Scenes Data. Available online: http:\/\/www.ehu.es\/ccwintco\/index.php?title=HyperspectralRemoteSensingScenes.","key":"ref_36"},{"doi-asserted-by":"crossref","unstructured":"LeCun, Y., Bottou, L., Orr, G.B., and Muller, K. (1998). Efficient BackProp. Neural Networks: Tricks of the Trade (Outgrowth of a 1996 NIPS Workshop), Springer.","key":"ref_37","DOI":"10.1007\/3-540-49430-8_2"},{"unstructured":"Soudry, D., and Carmon, Y. (2016). No bad local minima: Data independent training error guarantees for multilayer neural networks. arXiv.","key":"ref_38"},{"unstructured":"Li, Y., and Yuan, Y. (2017). Convergence Analysis of Two-layer Neural Networks with ReLU Activation. arXiv.","key":"ref_39"},{"unstructured":"Arora, S., Cohen, N., Golowich, N., and Hu, W. (2018). A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks. arXiv.","key":"ref_40"},{"unstructured":"Shamir, O. (2019). Exponential Convergence Time of Gradient Descent for One-Dimensional Deep Linear Neural Networks. arXiv.","key":"ref_41"},{"unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTAT), Sardinia, Italy.","key":"ref_42"},{"unstructured":"The Consultative Committee for Space Data Systems (2015, May 20). Hyperspectral and Multispectral Test Images. Available online: http:\/\/cwe.ccsds.org\/sls\/docs\/sls-dc\/123.0-B-Info\/TestData.","key":"ref_43"},{"doi-asserted-by":"crossref","unstructured":"Abrardo, A., Barni, M., Bertoli, A., and Grimoldi, R. (2011). Low-Complexity Approaches for Lossless and Near-Lossless Hyperspectral Image Compression. Satellite Data Compression, Springer.","key":"ref_44","DOI":"10.1007\/978-1-4614-1183-3_3"},{"unstructured":"CCSDS (2017, September 01). Image Data Compression CCSDS 122.0-B-2, ser. Blue Book, September 2017. Available online: https:\/\/public.ccsds.org\/Pubs\/122x0b2.pdf.","key":"ref_45"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/6\/6\/38\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:33:38Z","timestamp":1760175218000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/6\/6\/38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,28]]},"references-count":45,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["jimaging6060038"],"URL":"https:\/\/doi.org\/10.3390\/jimaging6060038","relation":{},"ISSN":["2313-433X"],"issn-type":[{"type":"electronic","value":"2313-433X"}],"subject":[],"published":{"date-parts":[[2020,5,28]]}}}