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Earth Science Informatics

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17.05.2021 | Review Article

Hyperspectral and multispectral image fusion techniques for high resolution applications: a review

verfasst von: Dioline Sara, Ajay Kumar Mandava, Arun Kumar, Shiny Duela, Anitha Jude

Erschienen in: Earth Science Informatics | Ausgabe 4/2021

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Abstract

Hyperspectral imaging has been rapidly developing over the past decade, and modern sensor technologies can cover large areas with exceptional spatial, spectral, and temporal resolutions. Due to these features, hyperspectral imaging is used effectively in numerous remote sensing applications such as precision agriculture, environmental monitoring, food analysis, and military applications requiring estimation of physical parameters of many complex surfaces and identifying visually similar materials with acceptable spectral signatures. The scope of fusion of the two images, one with high spatial content and the other with high spectral content, is to estimate one image with high spatial and spectral content. This paper presents a brief review of recent image resolution fusion algorithms, including deep learning techniques, for hyperspectral images. The need of high resolution panchromatic (pan) and multispectral (MS) images, lossless registration of images from multiple sources and point spread function (PSF) impose limitations for performing pan sharpening process. Hence the fusion is achieved using multispectral images instead of panchromatic images on hyperspectral images. It is essential to identify and reduce uncertainties in the image processing chain to improve image fusion enhancement. This paper also presents the current practices, problems, and prospects of hyperspectral image fusion. In addition, some important issues affecting fusion performance are discussed.

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Akhtar N, Mian A (2018) Threat of adversarial attacks on deep learning in computer vision: a survey. IEEE Access 6:14410–14430. https://doi.org/10.1109/ACCESS.2018.2807385CrossRef

Amigo JM and Santos C (2020) ‘Preprocessing of hyperspectral and multispectral images’, in Data Handling in Science and Technology. doi: https://doi.org/10.1016/B978-0-444-63977-6.00003-1

Asokan A, Popescu DE, Anitha J, Hemanth DJ (2020a) Bat algorithm based non-linear contrast stretching for satellite image enhancement. Geosciences (Switzerland) 10(2):1–12. https://doi.org/10.3390/geosciences10020078CrossRef

Asokan A, Anitha J, Ciobanu M, Gabor A, Naaji A, Hemanth DJ (2020b) Image processing techniques for analysis of satellite images for historical maps classification-An overview. Applied Sciences (Switzerland) 10(12). https://doi.org/10.3390/app10124207

Asokan A et al. (2021) ‘Deep feature extraction and feature fusion for bi-temporal satellite image classification’, Computers, Materials and Continua, 66(1), pp. 373–388. Doi: https://doi.org/10.32604/cmc.2020.012364

Asokan A, Anitha J (2019) Change detection techniques for remote sensing applications: a survey. Earth Sci Inf 12:143–160. https://doi.org/10.1007/s12145-019-00380-5CrossRef

Azarang A and Ghassemian H (2017) ‘A new pansharpening method using multi resolution analysis framework and deep neural networks’, in 3rd International Conference on Pattern Analysis and Image Analysis, IPRIA 2017. doi: https://doi.org/10.1109/PRIA.2017.7983017

Bendoumi MA et al. (2012) ‘Unmixing approach for hyperspectral data resolution enhancement using high resolution multispectral image’, in 2012 12th International Conference on Control, Automation, Robotics and Vision, ICARCV 2012. doi: https://doi.org/10.1109/ICARCV.2012.6485345

Bendoumi MA, He M, Mei S (2014) Hyperspectral image resolution enhancement using high-resolution multispectral image based on spectral unmixing. IEEE Trans Geosci Remote Sens 52:6574–6583. https://doi.org/10.1109/TGRS.2014.2298056CrossRef

Blasch E, Zheng Y and Liu Z (2018) Multispectral Image Fusion and Colorization, Multispectral image fusion and colorization. doi: https://doi.org/10.1117/3.2316455

Borsoi RA, Imbiriba T, Bermudez JCM (2020) Super-resolution for Hyperspectral and multispectral image fusion accounting for seasonal spectral variability. IEEE Trans Image Process 29:116–127. https://doi.org/10.1109/TIP.2019.2928895CrossRef

Brunet D, Vrscay ER, Wang Z (2012) On the mathematical properties of the structural similarity index. IEEE Trans Image Process 21:1488–1499. https://doi.org/10.1109/TIP.2011.2173206CrossRef

Chakravortty S, Subramaniam P (2014) ‘Fusion of hyperspectral and multispectral image data for enhancement of spectral and spatial resolution’, in International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences - ISPRS Archives XL-8:1099–1103. https://doi.org/10.5194/isprsarchives-XL-8-1099-2014CrossRef

Chen Q, Shi Z, An Z (2014a) Hyperspectral image fusion based on sparse constraint NMF. Optik 125(2):832–838. https://doi.org/10.1016/j.ijleo.2013.07.061CrossRef

Chen Z, Pu H, Wang B, Jiang GM (2014b) Fusion of hyperspectral and multispectral images: a novel framework based on generalization of pan-sharpening methods. IEEE Geosci Remote Sens Lett 11:1418–1422. https://doi.org/10.1109/LGRS.2013.2294476CrossRef

Cheng G, Han J (2016) A survey on object detection in optical remote sensing images. ISPRS J Photogramm Remote Sens 117:11–28. https://doi.org/10.1016/j.isprsjprs.2016.03.014CrossRef

Dian R, Li S, Fang L, Wei Q (2019) Multispectral and hyperspectral image fusion with spatial-spectral sparse representation. Information Fusion. 49:262–270. https://doi.org/10.1016/j.inffus.2018.11.012CrossRef

Ding Z et al. (2017) ‘A Pan-sharpening method for multispectral image with back propagation neural network and its parallel optimization based on spark’, in Proceedings of 2017 International Conference on Progress in Informatics and Computing, PIC 2017. doi: https://doi.org/10.1109/PIC.2017.8359525

Ducournau A and Fablet R (2017) ‘Deep learning for ocean remote sensing: An application of convolutional neural networks for super-resolution on satellite-derived SST data’, in 2016 9th IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2016. doi: https://doi.org/10.1109/PRRS.2016.7867019

Eghbalian S, Ghassemian H (2018) Multi spectral image fusion by deep convolutional neural network and new spectral loss function. Int J Remote Sens 39:3983–4002. https://doi.org/10.1080/01431161.2018.1452074CrossRef

Eghbalian S and Ghassemian H (2019) ‘Multi spectral image fusion with deep convolutional network’, in 9th International Symposium on Telecommunication: With Emphasis on Information and Communication Technology, IST 2018. doi: https://doi.org/10.1109/ISTEL.2018.8661137

Feng CH et al (2018) Hyperspectral imaging and multispectral imaging as the novel techniques for detecting defects in raw and processed meat products: current state-of-the-art research advances. Food Control. https://doi.org/10.1016/j.foodcont.2017.07.013

Ferraris V, Dobigeon N, Wei Q, Chabert M (2018) Detecting changes between optical images of different spatial and spectral resolutions: a fusion-based approach. IEEE Trans Geosci Remote Sens 56:1566–1578. https://doi.org/10.1109/TGRS.2017.2765348CrossRef

Palsson F, Johannes R, Sveinsson MOU. and JA. (2014) ‘Model Based PCA / WAVELET Fusion Of Multispectral And Hyperspectral Images’, in IEEE Geoscience and Remote Sensing Symposium, pp. 1532–1535

Ghassemian H (2016) A review of remote sensing image fusion methods. Information Fusion. 32:75–89. https://doi.org/10.1016/j.inffus.2016.03.003CrossRef

Gomez RB, Jazaeri A and Kafatos M (2001) ‘Wavelet-based hyperspectral and multispectral image fusion’, in Geo-Spatial Image and Data Exploitation II, pp. 36–42. https://doi.org/10.1117/12.428249

Grohnfeldt C, Zhu XX and Bamler R (2013) ‘Jointly sparse fusion of hyperspectral and multispectral imagery’, in International Geoscience and Remote Sensing Symposium (IGARSS). doi: https://doi.org/10.1109/IGARSS.2013.6723732

Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J, Chen T (2018) Recent advances in convolutional neural networks. Pattern Recogn 77:354–377. https://doi.org/10.1016/j.patcog.2017.10.013CrossRef

Guerra R, Lopez S, Sarmiento R (2016) A computationally efficient algorithm for fusing multispectral and Hyperspectral images. IEEE Trans Geosci Remote Sens 54:5712–5728. https://doi.org/10.1109/TGRS.2016.2570433CrossRef

Han X, Yu J, Luo J, Sun W (2019) Hyperspectral and multispectral image fusion using cluster-based multi-branch BP neural networks. Remote Sens 11. https://doi.org/10.3390/rs11101173

Han X, Yu J, Xue JH, Sun W (2020) Hyperspectral and multispectral image fusion using optimized twin dictionaries. IEEE Trans Image Process 29:4709–4720. https://doi.org/10.1109/TIP.2020.2968773CrossRef

Han XH and Chen YW (2019) ‘Deep residual network of spectral and spatial fusion for hyperspectral image super-resolution’, in Proceedings - 2019 IEEE 5th International Conference on Multimedia Big Data, BigMM 2019. doi: https://doi.org/10.1109/BigMM.2019.00-13

He G, Xing S, Xia Z, Huang Q, Fan J (2018) Panchromatic and multi-spectral image fusion for new satellites based on multi-channel deep model’, in Machine Vision and Applications. 29:933–946. https://doi.org/10.1007/s00138-018-0964-5

Huang B et al (2014a) Spatial and spectral image fusion using sparse matrix factorization. IEEE Trans Geosci Remote Sens 52:1693–1704. https://doi.org/10.1109/TGRS.2013.2253612CrossRef

Huang PS et al. (2014b) ‘Deep learning for monaural speech separation’, in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. doi: https://doi.org/10.1109/ICASSP.2014.6853860

Jagalingam P, Hegde AV (2015) A review of quality metrics for fused image. Aquatic Procedia 4:133–142. https://doi.org/10.1016/j.aqpro.2015.02.019CrossRef

Kakooei M, Baleghi Y (2020) A two-level fusion for building irregularity detection in post-disaster VHR oblique images. Earth Sci Inf 13:459–477. https://doi.org/10.1007/s12145-020-00449-6CrossRef

Kamilaris A, Prenafeta-Boldú FX (2018) Deep learning in agriculture: a survey. Comput Electron Agric 147:70–90. https://doi.org/10.1016/j.compag.2018.02.016CrossRef

Kanatsoulis CI et al (2018) ‘Hyperspectral super-resolution via coupled tensor factorization: Identifiability and algorithms’, in ICASSP. IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. https://doi.org/10.1109/ICASSP.2018.8462525

Kim Y, Choi J, Han D, Kim Y (2015) Block-based fusion algorithm with simulated band generation for Hyperspectral and multispectral images of partially different wavelength ranges. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8:2997–3007. https://doi.org/10.1109/JSTARS.2015.2433673CrossRef

Kussul N, Lavreniuk M, Skakun S, Shelestov A (2017) Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci Remote Sens Lett 14:778–782. https://doi.org/10.1109/LGRS.2017.2681128CrossRef

Li S, Kang X, Fang L, Hu J, Yin H (2017) Pixel-level image fusion: a survey of the state of the art. Information Fusion. 33:100–112. https://doi.org/10.1016/j.inffus.2016.05.004CrossRef

Li S, Dian R, Fang L, Bioucas-Dias JM (2018) Fusing Hyperspectral and multispectral images via coupled sparse tensor factorization. IEEE Trans Image Process 27:4118–4130. https://doi.org/10.1109/TIP.2018.2836307CrossRef

Liang J, Zhang Y and Mei S (2017) ‘Hyperspectral and multispectral image fusion using dual-source localized dictionary pair’, in 2017 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2017 - Proceedings. doi: https://doi.org/10.1109/ISPACS.2017.8266485

Lin B, Tao X, Xu M, Dong L, Lu J (2017) Bayesian Hyperspectral and multispectral image fusions via double matrix factorization. IEEE Trans Geosci Remote Sens 55:5666–5678. https://doi.org/10.1109/TGRS.2017.2711640CrossRef

Lin B, Tao X, Duan Y, Lu J (2018) Hyperspectral and multispectral image fusion based on low rank constrained Gaussian mixture model. IEEE Access. 6:16901–16910. https://doi.org/10.1109/ACCESS.2018.2817071CrossRef

Liu Y, Chen X, Wang Z, Wang ZJ, Ward RK, Wang X (2018) Deep learning for pixel-level image fusion: recent advances and future prospects. Information Fusion. 42:158–173. https://doi.org/10.1016/j.inffus.2017.10.007CrossRef

Ma L, Liu Y, Zhang X, Ye Y, Yin G, Johnson BA (2019) Deep learning in remote sensing applications: a meta-analysis and review. ISPRS J Photogramm Remote Sens 152:166–177. https://doi.org/10.1016/j.isprsjprs.2019.04.015CrossRef

Masi G, Cozzolino D, Verdoliva L, Scarpa G (2016) Pansharpening by convolutional neural networks. Remote Sens 8. https://doi.org/10.3390/rs8070594

Mayumi N and Iwasaki A (2011) ‘Image sharpening using hyperspectral and multispectral data’, in International Geoscience and Remote Sensing Symposium (IGARSS). doi: https://doi.org/10.1109/IGARSS.2011.6049179

Mifdal J et al. (2017) ‘Hyperspectral and multispectral wasserstein barycenter for image fusion’, in International Geoscience and Remote Sensing Symposium (IGARSS). doi: https://doi.org/10.1109/IGARSS.2017.8127721

Moeller M, Wittman T and Bertozzi AL (2009) ‘A variational approach to hyperspectral image fusion’, in Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV doi: https://doi.org/10.1117/12.818243

Nezhad, Z. H. et al. (2016) ‘Superresolution of hyperspectral images using spectral unmixing and sparse regularization’, in International Geoscience and Remote Sensing Symposium (IGARSS). doi: https://doi.org/10.1109/IGARSS.2016.7730882

Nikolakopoulos K, Oikonomidis D (2015) Quality assessment of ten fusion techniques applied on worldview-2. European Journal of Remote Sensing 48:141–167. https://doi.org/10.5721/EuJRS20154809CrossRef

Palsson F, Sveinsson JR, Ulfarsson MO, Benediktsson JA (2015) Model-based fusion of multi-and hyperspectral images using PCA and wavelets. IEEE Trans Geosci Remote Sens 53:2652–2663. https://doi.org/10.1109/TGRS.2014.2363477CrossRef

Palsson F, Sveinsson JR, Ulfarsson MO (2017) Multispectral and Hyperspectral image fusion using a 3-D-convolutional neural network. IEEE Geosci Remote Sens Lett 14:639–643. https://doi.org/10.1109/LGRS.2017.2668299CrossRef

Pohl C, Van Genderen JL (1998) Review article multisensor image fusion in remote sensing: concepts, methods and applications. Int J Remote Sens 19:823–854. https://doi.org/10.1080/014311698215748CrossRef

Radhakrishnan S, Lakshminarayanan AS, Chatterjee JM, Hemanth DJ (2020) Forest data visualization and land mapping using support vector machines and decision trees. Earth Sci Inf 13(4):1119–1137. https://doi.org/10.1007/s12145-020-00492-3CrossRef

Rao Y, He L and Zhu J (2017) ‘A residual convolutional neural network for pan-shaprening’, in RSIP 2017 - International Workshop on Remote Sensing with Intelligent Processing, Proceedings. doi: https://doi.org/10.1109/RSIP.2017.7958807

Razzak MI, Naz S and Zaib A (2018) ‘Deep learning for medical image processing: overview, challenges and the future BT - classification in BioApps: automation of decision making’, Springer. doi: https://doi.org/10.1007/978-3-319-65981-7_12

Rezaei H, Karami A and Scheunders P (2018) ‘Hyperspectral and multispectral image fusion based on spectral matching in the Shearlet domain’, in International Geoscience and Remote Sensing Symposium (IGARSS). doi: https://doi.org/10.1109/IGARSS.2018.8518922

Seifi Majdar R, Ghassemian H (2020) A probabilistic framework for weighted combination of multiple-feature classifications of hyperspectral images. Earth Sci Inf 13:55–69. https://doi.org/10.1007/s12145-019-00411-1CrossRef

Sheoran S, Mittal N, Gelbukh A (2020) Analysis on application of swarm-based techniques in processing remote sensed data. Earth Sci Inf 13:97–113. https://doi.org/10.1007/s12145-019-00417-9CrossRef

Sylla D, Minghelli-Roman A, Blanc P, Mangin A, Hembise Fanton d'Andon O (2014) Fusion of multispectral images by extension of the pan-sharpening ARSIS method. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 7:1781–1791. https://doi.org/10.1109/JSTARS.2013.2271911CrossRef

Wang W et al. (2019) ‘Deep blind hyperspectral image fusion’, in Proceedings of the IEEE International Conference on Computer Vision. doi: https://doi.org/10.1109/ICCV.2019.00425

Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13:600–612. https://doi.org/10.1109/TIP.2003.819861CrossRef

Wei Q et al. (2014) ‘Fusion of multispectral and hyperspectral images based on sparse representation’, in European Signal Processing Conference

Wei Q, Bioucas-Dias J, Dobigeon N, Tourneret JY (2015) Hyperspectral and multispectral image fusion based on a sparse representation. IEEE Trans Geosci Remote Sens 53:3658–3668. https://doi.org/10.1109/TGRS.2014.2381272CrossRef

Wei Q, Dobigeon N, Tourneret JY, Bioucas-Dias J, Godsill S (2016) R-FUSE: robust fast fusion of multiband images based on solving a Sylvester equation. IEEE Signal Processing Letters 23:1632–1636. https://doi.org/10.1109/LSP.2016.2608858CrossRef

Wei Q, Dobigeon N, Tourneret JY (2014a) ‘Bayesian fusion of hyperspectral and multispectral images’, in ICASSP. IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. https://doi.org/10.1109/ICASSP.2014.6854186

Wei Q, Dobigeon N and Tourneret JY (2014b) ‘Bayesian fusion of multispectral and hyperspectral images with unknown sensor spectral response’, in 2014 IEEE International Conference on Image Processing, ICIP 2014. doi: https://doi.org/10.1109/ICIP.2014.7025140

Wei Q, Dobigeon N, Tourneret JY (2015a) Bayesian fusion of multi-band images. IEEE Journal on Selected Topics in Signal Processing 9:1117–1127. https://doi.org/10.1109/JSTSP.2015.2407855CrossRef

Wei Q, Dobigeon N and Tourneret JY (2015b) ‘Bayesian fusion of multispectral and hyperspectral images using a block coordinate descent method’, in Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing doi: https://doi.org/10.1109/WHISPERS.2015.8075373

Wei Q, Dobigeon N, Tourneret JY (2015c) Fast fusion of multi-band images based on solving a Sylvester equation. IEEE Trans Image Process 24:4109–4121. https://doi.org/10.1109/TIP.2015.2458572CrossRef

Winter ME et al. (2006) ‘High-performance fusion of multispectral and hyperspectral data’, in Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII doi: https://doi.org/10.1117/12.668622

Xie F, Shi M, Shi Z, Yin J, Zhao D (2017) Multilevel cloud detection in remote sensing images based on deep learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 10:3631–3640. https://doi.org/10.1109/JSTARS.2017.2686488CrossRef

Xie Q et al. (2019) ‘Multispectral and hyperspectral image fusion by MS/HS fusion net’, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, pp. 1585–1594. doi: https://doi.org/10.1109/CVPR.2019.00168

Xu Y, Wu Z, Chanussot J, Comon P, Wei Z (2020) Nonlocal coupled tensor CP decomposition for Hyperspectral and multispectral image fusion. IEEE Trans Geosci Remote Sens 58:348–362. https://doi.org/10.1109/TGRS.2019.2936486CrossRef

Yang J, Zhao YQ, Chan JCW (2018) Hyperspectral and multispectral image fusion via deep two-branches convolutional neural network. Remote Sens 10. https://doi.org/10.3390/rs10050800

Yang Q et al. (2019a) ‘Hyperspectral and multispectral image fusion based on deep attention network’, in Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing. doi: https://doi.org/10.1109/WHISPERS.2019.8920825

Yang W, Zhang X, Tian Y, Wang W, Xue JH, Liao Q (2019b) Deep learning for single image super-resolution: a brief review. IEEE Transactions on Multimedia 21:3106–3121. https://doi.org/10.1109/TMM.2019.2919431CrossRef

Yokoya N, Grohnfeldt C, Chanussot J (2017) Hyperspectral and multispectral data fusion: a comparative review of the recent literature. IEEE Geoscience and Remote Sensing Magazine. 5:29–56. https://doi.org/10.1109/MGRS.2016.2637824CrossRef

Yokoya N, Yairi T, Iwasaki A (2012) Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion. IEEE Trans Geosci Remote Sens 50:528–537. https://doi.org/10.1109/TGRS.2011.2161320CrossRef

Young T, Hazarika D, Poria S, Cambria E (2018) Recent trends in deep learning based natural language processing [review article]. IEEE Comput Intell Mag 13:55–75. https://doi.org/10.1109/MCI.2018.2840738CrossRef

Zehtabian A, Ghassemian H (2017) An adaptive framework for spectral-spatial classification based on a combination of pixel-based and object-based scenarios. Earth Sci Inf 10:357–368. https://doi.org/10.1007/s12145-017-0298-2CrossRef

Zhang C, Patras P, Haddadi H (2019) Deep learning in Mobile and wireless networking: a survey. IEEE Communications Surveys and Tutorials 21:2224–2287. https://doi.org/10.1109/COMST.2019.2904897CrossRef

Zhang F, Zhang K (2020) Superpixel guided structure sparsity for multispectral and hyperspectral image fusion over couple dictionary. Multimed Tools Appl 79:4949–4964. https://doi.org/10.1007/s11042-019-7188-1CrossRef

Zhang JS, Cao J and Mao B (2017a) ‘Application of deep learning and unmanned aerial vehicle technology in traffic flow monitoring’, in Proceedings of 2017 International Conference on Machine Learning and Cybernetics, ICMLC 2017. doi: https://doi.org/10.1109/ICMLC.2017.8107763

Zhang K, Wang M, Yang S, Xing Y, Qu R (2016) Fusion of panchromatic and multispectral images via coupled sparse non-negative matrix factorization. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9:5740–5747. https://doi.org/10.1109/JSTARS.2015.2475754CrossRef

Zhang K, Wang M, Yang S, Jiao L (2018a) Spatial-spectral-graph-regularized low-rank tensor decomposition for multispectral and Hyperspectral image fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 11:1030–1040. https://doi.org/10.1109/JSTARS.2017.2785411CrossRef

Zhang K, Wang M, Yang S (2017b) Multispectral and Hyperspectral image fusion based on group spectral embedding and low-rank factorization. IEEE Trans Geosci Remote Sens 55:1363–1371. https://doi.org/10.1109/TGRS.2016.2623626CrossRef

Zhang Q, Yang LT, Chen Z, Li P (2018b) A survey on deep learning for big data. Information Fusion 42:146–157. https://doi.org/10.1016/j.inffus.2017.10.006CrossRef

Zhang Y (2012) Wavelet-based Bayesian fusion of multispectral and hyperspectral images using Gaussian scale mixture model. Int J Image Data Fusion 3:23–37. https://doi.org/10.1080/19479832.2010.551522CrossRef

Zhang Y, Gao Y, et al. (2015a) ‘Hyperspectral and multispectral image fusion based on constrained CNMF unmixing’, in Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing. doi: https://doi.org/10.1109/WHISPERS.2015.8075463

Zhang Y, Wang Y, et al. (2015b) ‘Hyperspectral and multispectral image fusion using CNMF with minimum endmember simplex volume and abundance sparsity constraints’, in International Geoscience and Remote Sensing Symposium (IGARSS). doi: https://doi.org/10.1109/IGARSS.2015.7326172

Zhang Y, De Backer S, Scheunders P (2009) Noise-resistant wavelet-based Bayesian fusion of multispectral and hyperspectral images. IEEE Trans Geosci Remote Sens 47:3834–3843. https://doi.org/10.1109/TGRS.2009.2017737CrossRef

Zhang Y, DeBacker S and Scheunders P (2008) ‘Bayesian fusion of multispectral and hyperspectral image inwavelet domain’, in International Geoscience and Remote Sensing Symposium (IGARSS). doi: https://doi.org/10.1109/IGARSS.2008.4780029

Zhang Y, He M (2007) Multi-spectral and hyperspectral image fusion using 3-D wavelet transform. J Electron 24:218–224. https://doi.org/10.1007/s11767-005-0232-5CrossRef

Zhang Y, Mei S and He M (2011) ‘Bayesian fusion of hyperspectral and multispectral images using Gaussian scale mixture prior’, in International Geoscience and Remote Sensing Symposium (IGARSS). doi: https://doi.org/10.1109/IGARSS.2011.6049727

Zhang Y, Zhao T and He M (2018c) ‘Hyperspectral and multispectral image fusion using local spatial-spectral dictionary pair’, in Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017. doi: https://doi.org/10.1109/APSIPA.2017.8282051

Zhao T et al. (2016) ‘Hyperspectral and multispectral image fusion using collaborative representation with local adaptive dictionary pair’, in International Geoscience and Remote Sensing Symposium (IGARSS). doi: https://doi.org/10.1109/IGARSS.2016.7730881

Zhong J, Yang B, Huang G, Zhong F, Chen Z (2016) Remote sensing image fusion with convolutional neural network. Sensing and Imaging 17. https://doi.org/10.1007/s11220-016-0135-6

Zhou F, Hang R, Liu Q, Yuan X (2019) Pyramid fully convolutional network for Hyperspectral and multispectral image fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 12:1549–1558. https://doi.org/10.1109/JSTARS.2019.2910990CrossRef

Zhou Y, Feng L, Hou C, Kung SY (2017) Hyperspectral and multispectral image fusion based on local low rank and coupled spectral Unmixing. IEEE Trans Geosci Remote Sens 55:5997–6009. https://doi.org/10.1109/TGRS.2017.2718728CrossRef

Zhu XX, Tuia D, Mou L, Xia GS, Zhang L, Xu F, Fraundorfer F (2017) Deep learning in remote sensing: a comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine 5:8–36. https://doi.org/10.1109/MGRS.2017.2762307CrossRef

Zhu Y, Zhang Y, Zhang H, Yang J, Zhao Z (2020) Data augmentation of X-ray images in baggage inspection based on generative adversarial networks. IEEE Access 8:86536–86544. https://doi.org/10.1109/ACCESS.2020.2992861CrossRef

Titel: Hyperspectral and multispectral image fusion techniques for high resolution applications: a review
verfasst von: Dioline Sara
Ajay Kumar Mandava
Arun Kumar
Shiny Duela
Anitha Jude
Publikationsdatum: 17.05.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Earth Science Informatics / Ausgabe 4/2021
Print ISSN: 1865-0473
Elektronische ISSN: 1865-0481
DOI: https://doi.org/10.1007/s12145-021-00621-6

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