Abstract
Recent advances in the sensors technology for imaging spectroscopy coupled with high computing power, raise the demand to develop the algorithms for processing and analysis of hyperspectral data for various applications. Well known techniques and algorithms are available for processing multispectral data in the literature. Researchers tried to use similar approaches for hyperspectral data analysis and succeeded up to some extent. Several techniques for atmospheric correction, dimensionality reduction, endmember extraction and classification has been developed and reported accordingly. To process and evaluate the hyperspectral data for domain applications require generalized framework. This article critically reviews most of the existing hyperspectral data processing and analysis approaches and gives generalized framework. Which offers considerate view for future potential and focuses emerging challenges in the development of robust algorithms for hyperspectral data processing and analysis.
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References
Shippert P (2004) Why use hyperspectral imagery? Photogramm Eng Remote Sens 70(4):377–396
Mohan BK, Porwal A (2015) Hyperspectral image processing and analysis. Curr Sci 108:833–841
Green R, Landeen S, McCubbin I, Thompson D, Bue B (2015) Airborne visible/infrared imaging spectrometer next generation (AVIRIS-NG), 1st edn. [PDF] JPL, California Institute of Technology. http://vedas.sac.gov.in:8080/aviris/pdf/20150726_AVRISINGDataGuide_v4.pdf. Accessed 1 July 2017
Kruse FA (2004) Comparison of ATREM, ACORN, and FLAASH atmospheric corrections using low-altitude AVIRIS data of Boulder, CO. In: Summaries of 13th JPL airborne geoscience workshop, Jet Propulsion Lab, Pasadena, CA
Tuominen J, Lipping T (2011) Atmospheric correction of hyperspectral data using combined empirical and model based method. In: Proceedings of the 7th European association of remote sensing laboratories SIG-imaging spectroscopy workshop, Edinburgh, Scotland, UK, vol 1113
Ben-Dor E, Kindel B, Goetz AFH (2004) Quality assessment of several methods to recover surface reflectance using synthetic imaging spectroscopy data. Remote Sens Environ 90(3):389–404
Kumar MV, Yarrakula K (2017) Comparison of efficient techniques of hyper-spectral image preprocessing for mineralogy and vegetation studies
Thompson DR, Roberts DA, Gao BC, Green RO, Guild L, Hayashi K, Kudela R, Palacios S (2016) Atmospheric correction with the Bayesian empirical line. Opt Express 24(3):2134–2144
San A BT, Suzen B ML (2010) Evaluation of different atmospheric correction algorithms for EO-1 Hyperion imagery. Int Arch Photogram Remote Sens Spat Inf Sci 38:392–397
Bernstein LS et al (2012) Quick atmospheric correction code: algorithm description and recent upgrades. Opt Eng 51(11):111719-1
Pervez W, Khan SA (2015) Hyperspectral hyperion imagery analysis and its application using spectral analysis. Int Arch Photogram Remote Sens Spat Inf Sci 40(3):169
Bernstein LS, Adler-Golden SM, Jin X, Gregor B, Sundberg RL (2012) Quick atmospheric correction (QUAC) code for VNIR-SWIR spectral imagery: algorithm details. In: 2012 4th workshop on hyperspectral image and signal processing (WHISPERS). IEEE, pp 1–4
Carr SB, Bernstein LS, Adler-Golden SM (2015) The quick atmospheric correction (QUAC) algorithm for hyperspectral image processing: extending QUAC to a coastal scene. In: 2015 international conference on digital image computing: techniques and applications (DICTA). IEEE, pp 1–8
Carr SB Quick atmospheric correction (QUAC) of worldview-3 multispectral imagery—a comparison to hyperspectral imagery results
San BT, Suzen ML (2010) Evaluation of different atmospheric correction algorithms for EO-1 Hyperion imagery. Int Arch Photogram Remote Sens Spat Inf Sci 38(Part 8):392–397
Denisova A, Myasnikov V (2017) Atmospheric correction of hyperspectral images using qualitative information about registered scene. In: Ninth international conference on machine vision. International Society for Optics and Photonics, pp 1034125–1034125
Thompson DR, Gao BC, Green RO, Roberts DA, Dennison PE, Lundeen SR (2015) Atmospheric correction for global mapping spectroscopy: ATREM advances for the HyspIRI preparatory campaign. Remote Sens Environ 167:64–77
Seidel FC, Kokhanovsky AA, Schaepman ME (2010) Fast and simple model for atmospheric radiative transfer. Atmos Meas Tech 3:1129–1141
Gao BC, Heidebrecht KB, Goetz AF (1993) Derivation of scaled surface reflectances from AVIRIS data. Remote Sens Environ 44(2–3):165–178
Gao BC, Montes MJ, Davis CO, Goetz AF (2009) Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean. Remote Sens Environ 113:S17–S24
Pflug B, Main-Knorn M (2014) Validation of atmospheric correction algorithm ATCOR. In: SPIE proceedings lidar, radar and passive atmospheric measurements II, vol 9242, no 92420W, pp 1–8
Richter R, Center RSD (2004) ATCOR: atmospheric and topographic correction. DLR-German Aerospace Center. Remote Sensing Data Center
Daniel Schlaepfer I (2017) Atmospheric & topographic correction: the ATCOR models. [online] Rese.ch. http://www.rese.ch/products/atcor/. Accessed 20 June 2017
Schläpfer D, Richter R (2015) Recent developments in ATCOR for atmospheric compensation and radiometric processing of imaging spectroscopy data. EARSeL eProceedings 10(2015):1
Manakos I, Manevski K, Kalaitzidis C, Edler D (2011) Comparison between atmospheric correction modules on the basis of Worldview-2 imagery and in situ spectroradiometric measurements. In: Proceedings on 7th EARSeL SIG imaging spectroscopy workshop, pp 1–12
Lantzanakis G, Mitraka Z, Chrysoulakis N (2017) Comparison of physically and image based atmospheric correction methods for Sentinel-2 satellite imagery. In: Karacostas T, Bais A, Nastos P (eds) Perspectives on atmospheric sciences. Springer, Cham, 255–261
Sterckx S, Vreys K, Biesemans J, Iordache MD, Bertels L, Meuleman K (2016) Atmospheric correction of APEX hyperspectral data. Misc Geogr 20(1):16–20
Adler-Golden SM, Matthew MW, Bernstein LS, Levine RY, Berk A, Richtsmeier SC, Hoke ML (1999) Atmospheric correction for shortwave spectral imagery based on MODTRAN4. In: SPIE’s international symposium on optical science, engineering, and instrumentation. International Society for Optics and Photonics, pp 61–69
Goetz AF, Ferri M, Kindel B, Qu Z (2002) Atmospheric correction of Hyperion data and techniques for dynamic scene correction. In: 2002 IEEE international geoscience and remote sensing symposium, 2002. IGARSS’02, vol 3. IEEE, pp 1408–1410
Qu Z, Kindel BC, Goetz AF (2003) The high accuracy atmospheric correction for hyperspectral data (HATCH) model. IEEE Trans Geosci Remote Sens 41(6):1223–1231
Dong Y, Du B, Zhang L, Zhang L (2017) Dimensionality reduction and classification of hyperspectral images using ensemble discriminative local metric learning. IEEE Trans Geosci Remote Sens 55(5):2509–2524
Wang J, Chang CI (2006) Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis. IEEE Trans Geosci Remote Sens 44(6):1586–1600
Ringnér M (2008) What is principal component analysis? Nat Biotechnol 26(3):303
Koonsanit K, Jaruskulchai C, Eiumnoh A (2012) Band selection for dimension reduction in hyper spectral image using integrated information gain and principal components analysis technique. Int J Mach Learn Comput 2(3):248
Lodha SP, Kamlapur SM (2014) Dimensionality reduction techniques for hyperspectral images. Int J Appl Innov Eng Manage (IJAIEM) 3(10):92–99
Rodarmel C, Shan J (2002) Principal component analysis for hyperspectral image classification. Surv Land Inf Sci 62(2):115
Wu Z, Li Y, Plaza A, Li J, Xiao F, Wei Z (2016) Parallel and distributed dimensionality reduction of hyperspectral data on cloud computing architectures. IEEE J Sel Top Appl Earth Obs Remote Sens 9(6):2270–2278
Phillips RD, Watson LT, Blinn CE, Wynne R. H (2008) An adaptive noise reduction technique for improving the utility of hyperspectral data. In: Proceedings of the 17th William T. Pecora memorial remote sensing symposium, pp 16–20
Rajagopal R, Ranganathan V (2017) Evaluation of effect of unsupervised dimensionality reduction techniques on automated arrhythmia classification. Biomed Signal Process Control 34:1–8
Hyvarinen A (2013) Independent component analysis: recent advances. Philos Trans R Soc A 371(1984):20110534
Hyvärinen A, Oja E (2000) Independent component analysis: algorithms and applications. Neural Netw 13(4):411–430
Martínez PJ, Pérez RM, Plaza A, Aguilar PL, Cantero MC, Plaza J (2006) Endmember extraction algorithms from hyperspectral images. Ann Geophys 49(1):93–101
Kowkabi F, Ghassemian H, Keshavarz A (2016) A fast spatial–spectral preprocessing module for hyperspectral endmember extraction. IEEE Geosci Remote Sens Lett 13(6):782–786
Boardman JW, Kruse FA, Green RO (1995) Mapping target signatures via partial unmixing of AVIRIS data
Plaza A, Sánchez-Testal JJ, Plaza J, Valencia D (2005) An experimental evaluation of endmember generation algorithms. In: Optics east 2005. International Society for Optics and Photonics, pp 599501–599501
Chang CI, Plaza A (2006) A fast iterative algorithm for implementation of pixel purity index. IEEE Geosci Remote Sens Lett 3(1):63–67
Winter ME (1999) N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data. In: SPIE’s international symposium on optical science, engineering, and instrumentation. International Society for Optics and Photonics, pp 266–275
Gonzalez C, Mozos D, Resano J, Plaza A (2011) FPGA implementation of endmember extraction algorithms from hyperspectral imagery: pixel purity index versus N-FINDR. In: SPIE remote sensing. International Society for Optics and Photonics, pp 81830F–81830F
Nascimento JM, Dias JM (2005) Vertex component analysis: a fast algorithm to unmix hyperspectral data. IEEE Trans Geosci Remote Sens 43(4):898–910
Plaza A, Martínez P, Pérez R, Plaza J (2004) A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data. IEEE Trans Geosci Remote Sens 42(3):650–663
Plaza J, Hendrix EM, García I, Martín G, Plaza A (2012) On endmember identification in hyperspectral images without pure pixels: a comparison of algorithms. J Math Imaging Vis 42(2):163–175
Ifarraguerri A, Chang CI (1999) Multispectral and hyperspectral image analysis with convex cones. IEEE Trans Geosci Remote Sens 37(2):756–770
Boardman JW (1993) Automating spectral unmixing of AVIRIS data using convex geometry concepts
Chang CI, Wu CC, Liu W, Ouyang YC (2006) A new growing method for simplex-based endmember extraction algorithm. IEEE Trans Geosci Remote Sens 44(10):2804–2819
Chang CI, Chen SY, Li HC, Chen HM, Wen CH (2016) Comparative study and analysis among ATGP, VCA, and SGA for finding endmembers in hyperspectral imagery. IEEE J Sel Top Appl Earth Obs Remote Sens 9(9):4280–4306
Ji L, Geng X, Sun K, Zhao Y, Gong P (2015) Modified N-FINDR endmember extraction algorithm for remote-sensing imagery. Int J Remote Sens 36(8):2148–2162
Plaza A, Martínez P, Pérez R, Plaza J (2002) Spatial/spectral endmember extraction by multidimensional morphological operations. IEEE Trans Geosci Remote Sens 40(9):2025–2041
Goetz AFH (2001) Progress in hyperspectral imaging of vegetation. In Optics in Agriculture: 1990-2000: A Critical Review. Int Soc Opt Photonics. http://dx.doi.org/10.1117/12.420098
Neville R (1999) Automatic endmember extraction from hyperspectral data for mineral exploration. In: International airborne remote sensing conference and exhibition, 4th/21st Canadian symposium on remote sensing, Ottawa, Canada
Heinz DC (2001) Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery. IEEE Trans Geosci Remote Sens 39(3):529–545
Rogge DM, Rivard B, Zhang J, Sanchez A, Harris J, Feng J (2007) Integration of spatial–spectral information for the improved extraction of endmembers. Remote Sens Environ 110(3):287–303
Adams JB, Gillespie AR (2006) Remote sensing of landscapes with spectral images: a physical modeling approach. Cambridge University Press, Cambridge
Clark RN, Roush TL (1984) Reflectance spectroscopy: quantitative analysis techniques for remote sensing applications. J Geophys Res Solid Earth 89(B7):6329–6340
Tsai F, Philpot W (1998) Derivative analysis of hyperspectral data. Remote Sens Environ 66(1):41–51
Roberts DA, Roth KL, Perroy RL (2016) Hyperspectral Vegetation Indices. In : Thenkabail PS, Lyon JG, Huete A (eds) Hyperspectral remote sensing of vegetation. Crc press, London, pp 309–327
Milton EJ (1987) Review article principles of field spectroscopy. Remote Sens 8(12):1807–1827
Magendran T, Sanjeevi S, Bhattacharya AK, Surada S (2011) Hyperspectral radiometry to estimate the grades of iron ores of Noamundi, India—a preliminary study. J Indian Soc Remote Sens 39(4):473–483
Team U (2017) USGS Spectral Library|USGS Spectroscopy Laboratory. [online] Speclab.cr.usgs.gov. https://speclab.cr.usgs.gov/spectral-lib.html. Accessed 20 June 2017
Speclib.jpl.nasa.gov (2017) Search—spectral library. [online] https://speclib.jpl.nasa.gov/search-1. Accessed 20 June 2017
Ghamisi P, Plaza J, Chen Y, Li J, Plaza AJ (2017) Advanced spectral classifiers for hyperspectral images: a review. IEEE Geosci Remote Sens Mag 5(1):8–32
Chutia D, Bhattacharyya DK, Sarma KK, Kalita R, Sudhakar S (2016) Hyperspectral remote sensing classifications: a perspective survey. Trans GIS 20(4):463–490
Camps-Valls G, Marsheva TVB, Zhou D (2007) Semi-supervised graph-based hyperspectral image classification. IEEE Trans Geosci Remote Sens 45(10):3044–3054
Amini S, Homayouni S, Safari A (2014) Semi-supervised classification of hyperspectral image using random forest algorithm. In: 2014 IEEE international geoscience and remote sensing symposium (IGARSS). IEEE, pp 2866–2869
Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 28(5):823–870
Nath SS, Mishra G, Kar J, Chakraborty S, Dey N (2014) A survey of image classification methods and techniques. In 2014 international conference on control, instrumentation, communication and computational technologies (ICCICCT). IEEE, pp 554–557
Magnussen S, Boudewyn P, Wulder M (2004) Contextual classification of Landsat TM images to forest inventory cover types. Int J Remote Sens 25(12):2421–2440
Amasyali MF, Ersoy OK (2011) Comparison of single and ensemble classifiers in terms of accuracy and execution time. In: 2011 international symposium on innovations in intelligent systems and applications (INISTA). IEEE, pp 470–474
Ghamisi P, Dalla Mura M, Benediktsson JA (2015) A survey on spectral–spatial classification techniques based on attribute profiles. IEEE Trans Geosci Remote Sens 53(5):2335–2353
Maulik U, Chakraborty D (2017) Remote Sensing Image Classification: a survey of support-vector-machine-based advanced techniques. IEEE Geosci Remote Sens Mag 5(1):33–52
Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens 42(8):1778–1790
Waske B, Benediktsson JA, Árnason K, Sveinsson JR (2009) Mapping of hyperspectral AVIRIS data using machine-learning algorithms. Can J Remote Sens 35(sup1):S106–S116
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Joelsson SR, Benediktsson JA, Sveinsson JR (2005) Random forest classifiers for hyperspectral data. In: Proceedings on 2005 IEEE international geoscience and remote sensing symposium, 2005. IGARSS’05, vol 1. IEEE, pp 4-pp
Luc B, Deronde B, Kempeneers P, Debruyn W, Provoost S, Sensing R, Observation E (2005) Optimized spectral angle Mapper classification of spatially heterogeneous dynamic dune vegetation, a case study along the Belgian coastline. In: 9th international symposium on physical measurements and signatures in remote sensing (ISPMSRS). Beijing, China, p 2005
El Rahman SA, Aliady WA, Alrashed NI (2015) Supervised classification approaches to analyze hyperspectral dataset. Int J Image Graph Signal Process 7(5):42
Van der Meer FD, De Jong S (2003) Spectral mapping methods: many problems, some solutions. In: Proceedings of the 3rd EARSeL workshop on imaging spectroscopy, Herrsching, Germany, pp 13–16
Mazer AS, Martin M, Lee M, Solomon JE (1988) Image processing software for imaging spectrometry data analysis. Remote Sens Environ 24(1):201–210
Schowengerdt RA (2006) Remote sensing: models and methods for image processing. Academic Press, Cambridge
Zhang L, Zhang L, Du B (2016) Deep learning for remote sensing data: a technical tutorial on the state of the art. IEEE Geosci Remote Sens Mag 4(2):22–40
Chen Y, Lin Z, Zhao X, Wang G, Gu Y (2014) Deep learning-based classification of hyperspectral data. IEEE J Sel Top Appl Earth Obs Remote Sens 7(6):2094–2107
Zhou W, Shao Z, Diao C, Cheng Q (2015) High-resolution remote-sensing imagery retrieval using sparse features by auto-encoder. Remote Sens Lett 6(10):775–783
Chen Y, Zhao X, Jia X (2015) Spectral–spatial classification of hyperspectral data based on deep belief network. IEEE J Sel Top Appl Earth Obs Remote Sens 8(6):2381–2392
Hu W, Huang Y, Wei L, Zhang F, Li H (2015) Deep convolutional neural networks for hyperspectral image classification. J Sens. https://doi.org/10.1155/2015/258619
Gao J (2008) Digital analysis of remotely sensed imagery. McGraw-Hill Professional, New York
Acknowledgements
Authors would like to acknowledge to Department of Science and Technology, Government of India, for providing financial assistance under major research project R. No. BDID/01/23/2014-HSRS/35 (ALG-IV). Authors also extend heartfelt gratitude to DST-FIST program and UGC for providing lab facilities under UGC SAP (II) DRS Phase-I F.No.-3-42/2009, Phase-II 4-15/2015/DRS-II to Department of Computer Science & IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad (MS) India.
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Kale, K.V., Solankar, M.M., Nalawade, D.B. et al. A Research Review on Hyperspectral Data Processing and Analysis Algorithms. Proc. Natl. Acad. Sci., India, Sect. A Phys. Sci. 87, 541–555 (2017). https://doi.org/10.1007/s40010-017-0433-y
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DOI: https://doi.org/10.1007/s40010-017-0433-y