{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:51:00Z","timestamp":1760241060725,"version":"build-2065373602"},"reference-count":62,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,18]],"date-time":"2019-11-18T00:00:00Z","timestamp":1574035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61801359","61571345","91538101","61501346","61502367","61701360"],"award-info":[{"award-number":["61801359","61571345","91538101","61501346","61502367","61701360"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013314","name":"111 project","doi-asserted-by":"publisher","award":["B08038"],"award-info":[{"award-number":["B08038"]}],"id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Young Talent fund of University Association for Science and Technology in Shaanxi of China","award":["20190103"],"award-info":[{"award-number":["20190103"]}]},{"name":"Special Financial Grant from the China Postdoctoral Science Foundation","award":["2019T120878"],"award-info":[{"award-number":["2019T120878"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["JB180104"],"award-info":[{"award-number":["JB180104"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2019JQ153","2016JQ6023","2016JQ6018"],"award-info":[{"award-number":["2019JQ153","2016JQ6023","2016JQ6018"]}]},{"name":"General Financial Grant from the China Postdoctoral Science Foundation","award":["2017M620440"],"award-info":[{"award-number":["2017M620440"]}]},{"name":"Yangtse Rive Scholar Bonus Schemes","award":["CJT1l60102"],"award-info":[{"award-number":["CJT1l60102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral (HS) imaging is conducive to better describing and understanding the subtle differences in spectral characteristics of different materials due to sufficient spectral information compared with traditional imaging systems. However, it is still challenging to obtain high resolution (HR) HS images in both the spectral and spatial domains. Different from previous methods, we first propose spectral constrained adversarial autoencoder (SCAAE) to extract deep features of HS images and combine with the panchromatic (PAN) image to competently represent the spatial information of HR HS images, which is more comprehensive and representative. In particular, based on the adversarial autoencoder (AAE) network, the SCAAE network is built with the added spectral constraint in the loss function so that spectral consistency and a higher quality of spatial information enhancement can be ensured. Then, an adaptive fusion approach with a simple feature selection rule is induced to make full use of the spatial information contained in both the HS image and PAN image. Specifically, the spatial information from two different sensors is introduced into a convex optimization equation to obtain the fusion proportion of the two parts and estimate the generated HR HS image. By analyzing the results from the experiments executed on the tested data sets through different methods, it can be found that, in CC, SAM, and RMSE, the performance of the proposed algorithm is improved by about 1.42%, 13.12%, and 29.26% respectively on average which is preferable to the well-performed method HySure. Compared to the MRA-based method, the improvement of the proposed method in in the above three indexes is 17.63%, 0.83%, and 11.02%, respectively. Moreover, the results are 0.87%, 22.11%, and 20.66%, respectively, better than the PCA-based method, which fully illustrated the superiority of the proposed method in spatial information preservation. All the experimental results demonstrate that the proposed method is superior to the state-of-the-art fusion methods in terms of subjective and objective evaluations.<\/jats:p>","DOI":"10.3390\/rs11222691","type":"journal-article","created":{"date-parts":[[2019,11,18]],"date-time":"2019-11-18T11:18:48Z","timestamp":1574075928000},"page":"2691","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Hyperspectral Pansharpening Based on Spectral Constrained Adversarial Autoencoder"],"prefix":"10.3390","volume":"11","author":[{"given":"Gang","family":"He","sequence":"first","affiliation":[{"name":"State Key Laboratory of Integrated Service Networks, School of Telecommunications Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5006-9530","authenticated-orcid":false,"given":"Jiaping","family":"Zhong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Service Networks, School of Telecommunications Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0851-6565","authenticated-orcid":false,"given":"Jie","family":"Lei","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Service Networks, School of Telecommunications Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunsong","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Service Networks, School of Telecommunications Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0335-3878","authenticated-orcid":false,"given":"Weiying","family":"Xie","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Service Networks, School of Telecommunications Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2241","DOI":"10.1109\/TGRS.2014.2358615","article-title":"Intrinsic Image Decomposition for Feature Extraction of Hyperspectral Images","volume":"53","author":"Kang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","first-page":"1","article-title":"Deep Hyperspectral Image Sharpening","volume":"53","author":"Dian","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2018.2844555","article-title":"Hyperspectral Image Reconstruction by Latent Low-rank Representation for Classification","volume":"15","author":"Pan","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5447","DOI":"10.1109\/TIP.2017.2740621","article-title":"Matched Shrunken Cone Detector (MSCD): Bayesian Derivations and Case Studies for Hyperspectral Target Detection","volume":"26","author":"Wang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.1016\/j.patcog.2010.01.016","article-title":"Segmentation and Classification of Hyperspectral Images Using Watershed Transformation","volume":"43","author":"Tarabalkaa","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3123","DOI":"10.1109\/TCYB.2015.2497711","article-title":"Hyperspectral Anomaly Detection by Graph Pixel Selection","volume":"46","author":"Yuan","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Xie, W., Lei, J., Cui, Y., Li, Y., and Du, Q. (2019). Hyperspectral Pansharpening with Deep Priors. IEEE Trans. Neural Netw. Learn. Syst.","DOI":"10.1109\/TNNLS.2019.2920857"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/MGRS.2016.2637824","article-title":"Hyperspectral and Multispectral Data Fusion: A Comparative Review","volume":"5","author":"Yokoya","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.neucom.2018.07.030","article-title":"Hyperspectral Pansharpening via Improved PCA Approach and Optimal Weightd Fusion Strategy","volume":"315","author":"Li","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5443","DOI":"10.1109\/TGRS.2018.2817393","article-title":"Target-Adaptive Cnn-Based Pansharpening","volume":"56","author":"Scarpa","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"591","DOI":"10.14358\/PERS.72.5.591","article-title":"MTF-tailored Multiscale Fusion of High Resolution MS and PAN Imagery","volume":"72","author":"Aiazzi","year":"2015","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1109\/LGRS.2017.2668299","article-title":"Multispectral and Hyperspectral Image Fusion Using a 3d-Convolutional Neural Network","volume":"14","author":"Palsson","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1847","DOI":"10.1109\/TGRS.2008.917131","article-title":"Bayesian Data Fusion for Adaptable Image Pansharpening","volume":"46","author":"Fasbender","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3834","DOI":"10.1109\/TGRS.2009.2017737","article-title":"Noiseresistant Wavelet-Based Bayesian Fusion of Multispectral and Hyperspectral Images","volume":"47","author":"Zhang","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5666","DOI":"10.1109\/TGRS.2017.2711640","article-title":"Bayesian Hyperspectral and Multispectral Image Fusions via Double Matrix Factorization","volume":"55","author":"Lin","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","first-page":"339","article-title":"Extracting Spectral Contrast in Landsat the Matic Mapper Image Data Using Selective Principal Component Analysis","volume":"55","author":"Kwarteng","year":"1989","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_17","unstructured":"Laben, C.A., and Brower, B.V. (2000). Process for Enhancing the Spatial Resolution of Multispectral Imagery Using Pansharpening. (6,011,875), U.S. Patent."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3230","DOI":"10.1109\/TGRS.2007.901007","article-title":"Improving Component Substitution Pansharpening through Multivariate Regression of MS+PAN Data","volume":"45","author":"Aiazzi","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lai, W., Huang, J., Ahuja, N., and Yang, M.H. (2017, January 21\u201326). Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.618"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2984","DOI":"10.1109\/JSTARS.2015.2420582","article-title":"Processing of Multi-Resolution Thermal Hyperspectral and Digital Color Data: Outcome of the 2014 IEEE GRSS Data Fusion Contest","volume":"8","author":"Liao","year":"2017","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.192463","article-title":"A Theory for Multiresolution Signal Decomposition: The Wavelet Representation","volume":"11","author":"Mallat","year":"1989","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1109\/TGRS.2011.2161320","article-title":"Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion","volume":"50","author":"Yokoya","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, Y., Liu, Y., Zhang, C., He, M., and Mei, S. (2015, January 26\u201331). Hyperspectral and Multispectral Image Fusion Using CNMF with Minimum Endmember Simplex Volume and Abundance Sparsity Constraints. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326172"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TSP.2017.2757914","article-title":"A Unified Convergence Analysis of The Multiplicative Update Algorithm for Regularized Nonnegative Matrix Factorization","volume":"66","author":"Zhao","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4285","DOI":"10.1109\/TGRS.2017.2690445","article-title":"Superpixel-Based Intrinsic Image Decomposition of Hyperspectral Images","volume":"55","author":"Jin","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","first-page":"1037","article-title":"A New Pan-Sharpening Method with Deep Neural Networks","volume":"12","author":"Wei","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1963","DOI":"10.1109\/JSTARS.2017.2655112","article-title":"Hyperspectral Image Super-Resolution by Transfer Learning","volume":"10","author":"Yuan","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5969","DOI":"10.1109\/TIP.2018.2862629","article-title":"Exploiting Clustering Manifold Structure for Hyperspectral Imagery Super-Resolution","volume":"27","author":"Lei","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","first-page":"28","article-title":"Super-Resolution for Gaofen-4 Remote Sensing Images","volume":"15","author":"Li","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","unstructured":"Tappen, M.F., Freeman, W.T., and Adelson, E.H. (1982). Recovering Intrinsic Images from a Single Image. Southwest Research Inst Report, Southwest Research Institute."},{"key":"ref_31","first-page":"1","article-title":"Lightness and Retinex Theory","volume":"61","author":"Land","year":"1971","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Lee, K.M. (2017, January 16\u201318). Enhanced Deep Residual Networks for Single Image Super-Resolution. Proceedings of the IEEE conference on computer vision and pattern recognition workshops, Anacapri, Italy.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J., and Lee, K. (2016, January 27\u201330). Deeply Recursive Convolutional Network for Image Super-Resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.181"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s11263-006-6852-x","article-title":"A Variational Model for P and XS Image Fusion","volume":"69","author":"Coloma","year":"2006","journal-title":"Int. J. Comput. Vis."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1016\/j.isprsjprs.2011.05.002","article-title":"Wavelength Selection and Spectral Discrimination for Paddy Rice, with Laboratory Measurements of Hyperspectral Leaf Reflectance","volume":"66","author":"Song","year":"2011","journal-title":"ISPRS J. Photogram. Rem. Sens."},{"key":"ref_36","first-page":"540","article-title":"Pan-Sharpening with a Hyper-Laplacian Penalty","volume":"69","author":"Jiang","year":"2015","journal-title":"Proc. IEEE Int. Conf. Comput. Vis."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4028","DOI":"10.1109\/TIP.2015.2458701","article-title":"Texture Synthesis Using the Structure Tensor","volume":"24","author":"Akl","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chakrabarti, Y.A., and Zickler, T. (2011, January 20\u201325). Statistics of Real-World Hyperspectral Images. Proceedings of the IEEE Conference Computer Vision Pattern Recognit (CVPR), Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995660"},{"key":"ref_39","unstructured":"Qi, X., Zhou, M., Zhao, Q., Meng, D., Zuo, W., and Xu, Z. (2019, January 16\u201320). Multispectral and Hyperspectral Image Fusion by MS\/HS Fusion Net. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"8011","DOI":"10.1109\/TGRS.2019.2917759","article-title":"DDLPS: Detail-Based Deep Laplacian Pansharpening for Hyperspectral Imagery","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.neunet.2019.08.012","article-title":"Spectral Constraint Adversarial Autoencoders Approach to Feature Representation in Hyperspectral Anomaly Detection","volume":"119","author":"Xie","year":"2019","journal-title":"Neural Netw. Off. J. Int. Neural Netw. Soc."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Yang, J., Fu, X., Hu, Y., Yue, H., Ding, X., and Paisley, J. (2017, January 22\u201329). Pannet: A Deep Network Architecture for Pan-Sharpening. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.193"},{"key":"ref_43","unstructured":"Ian, J.G., Jean, P.-A., Mehdi, M., Bing, X., David, W.-F., Sherjil, O., Aaron, C., and Yoshua, B. (2014). Generative Adversarial Networks. Proc. Adv. Neural Inf. Process. Syst., 2672\u20132680."},{"key":"ref_44","unstructured":"Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., and Frey, B. (2016). Adversarial autoencoders. arXiv."},{"key":"ref_45","first-page":"1261","article-title":"The Potential Energy of an Autoencoder","volume":"37","author":"Kamyshanska","year":"2015","journal-title":"Mach. Intell."},{"key":"ref_46","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-Encoding Variational Bayes. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image Super-Resolution Using Deep Convolutional Networks","volume":"38","author":"Dong","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A Fast Learning Algorithm for Deep Belief Nets","volume":"18","author":"Geoffrey","year":"2006","journal-title":"Neural Comput."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the Dimensionality of Data with Neural Networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_51","unstructured":"Hinton, G., and Zemel, R. (December, January 28). Autoencoders, Minimum Description Length and Helmholtz Free Energy. Proceedings of the 14th Neural Information Processing Systems (NIPS), Denver, CO, USA."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5060","DOI":"10.1109\/TIE.2017.2739691","article-title":"Multitask Autoencoder Model for Recovering Human Poses","volume":"65","author":"Yu","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2438","DOI":"10.1109\/LGRS.2015.2482520","article-title":"Unsupervised Spectral-Spatial Feature Learning with Stacked Sparse Autoencoder for Hyperspectral Imagery Classification","volume":"12","author":"Tao","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Kang, M., Ji, K., Leng, X., and Zhou, H. (2017). Synthetic Aperture Radar Target Recognition with Feature Fusion based on a Stacked Autoencoder. Sensors, 17.","DOI":"10.3390\/s17010192"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1109\/TGRS.2013.2241444","article-title":"Unsupervised Feature Learning for Aerial Scene Classification","volume":"52","author":"Cheriyadat","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zou, Y., and Shi, W. (2017, January 23\u201325). Dilated Convolution Neural Network with Leakyrelu for Environmental Sound Classification. Proceedings of the 22nd International Conference on Digital Signal Processing (DSP), London, UK.","DOI":"10.1109\/ICDSP.2017.8096153"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2359","DOI":"10.1364\/JOSAA.23.002359","article-title":"Frequency of Metamerism in Natural Scenes","volume":"23","author":"David","year":"2006","journal-title":"J. Opt. Soc. Am. A-Opt. Image Sci. Vis."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Imagequality Assessment: From Error Visibility to Structural Structural Similarity","volume":"13","author":"Zhou","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","article-title":"The Spectral Image Processing System (SIPS)-Interactive Visualization and Analysis of Imaging Spectrometer Data","volume":"44","author":"Kruse","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.rse.2007.07.028","article-title":"Recent Advances in Techniques for Hyperspectral Image Processing","volume":"113","author":"Plaza","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1109\/TGRS.2008.916211","article-title":"An Efficient Pan-Sharpening Method via a Combined Adaptive PCA Approach and Contourlets","volume":"46","author":"Shah","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1007\/s11045-016-0415-2","article-title":"Comprehensive Review on Fusion Techniques for Spatial Information Enhancement in Hyperspectral Imagery","volume":"27","author":"Mookambiga","year":"2016","journal-title":"Multidimensional Syst. Signal Process."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/22\/2691\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:35:24Z","timestamp":1760189724000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/22\/2691"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,18]]},"references-count":62,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["rs11222691"],"URL":"https:\/\/doi.org\/10.3390\/rs11222691","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,11,18]]}}}