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Analysis on application of swarm-based techniques in processing remote sensed data

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Abstract

The remote sensed satellite images are big repository of information and provide the coverage of large areas. However, these images may not be able to describe the finer details of area being covered. Satellite Image optimization is the process of augmenting the components of an image for better and effective interpretations from satellite images. In order to obtain better visibility properties to fetch more information, various artificial intelligence techniques can be considered for the optimization process. Finding out the best technique for optimization is a challenging and time-consuming task [U1]. In this paper, applications of swarm-based artificial intelligence techniques such as ant colony optimization, particle swarm optimization, bat algorithm, artificial bee colony algorithm etc. are being analysed to process the remote sensed data. The detailed comparison with respect to classifier, utility, images considered, and observation are discussed. The comprehensive analysis revealed that particle swarm optimization is the most widely used technique. Further, various application areas such as land-use land-cover are discussed with possibilities of future research [U2].

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References

  • Agrawal RK, Bawane NG (2015) Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery. Appl Soft Comput J 28:217–225. https://doi.org/10.1016/j.asoc.2014.11.052

    Article  Google Scholar 

  • Alizadeh Naeini A, Babadi M, Mirzadeh SMJ, Amini S (2018) Particle swarm optimization for object-based feature selection of VHSR satellite images. IEEE Geosci Remote Sens Lett 15:379–383. https://doi.org/10.1109/LGRS.2017.2789194

    Article  Google Scholar 

  • Alpaydin, E. (2009). Introduction to machine learning. MIT press

  • Ari, Ç., & Aksoy, S., 2010, July. Unsupervised classification of remotely sensed images using gaussian mixture models and particle swarm optimization. In Geoscience and remote sensing symposium (IGARSS), 2010 IEEE international (pp. 1859–1862). IEEE

  • Arora, P., Mishra, A., Panchal, V.K., 2011. Swarm intelligence for feature identification in natural terrain environment. Proc. - 2011 Int. Conf. Comput. Intell. Commun. Syst. CICN 2011 722–726. https://doi.org/10.1109/CICN.2011.157

  • Azarang A, Ghassemian H (2017) An adaptive multispectral image fusion using particle swarm optimization. 2017 25th Iran. Conf. Electr. Eng. ICEE 2017:1708–1712. https://doi.org/10.1109/IranianCEE.2017.7985325

    Article  Google Scholar 

  • Banerjee S, Bharadwaj A, Gupta D, Panchal VK (2012) Remote sensing image classification using artificial bee Colony algorithm 2012. Int. Jour. of Computer Science and Informatics2.3 pp:67–72

  • Bedawi SM, Kamel MS (2010) Segmentation of very high resoultion remote sensing imagery of urban areas using particle swarm optimization. 2010. Int Conf Image Analysis and Recognition:81–88

  • Bedawi, S.M., Kamel, M.S., 2011. Urban land-cover classification based on swarm intelligence from high resolution remote sensing imagery. 2011 Int. Conf. Remote Sensing, Environ. Transp. Eng. 5617–5620. 10.1109/RSETE.2011.5965626

  • Bhandari AK, Singh VK, Kumar A, Singh GK (2014a) Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Syst Appl 41:3538–3560. https://doi.org/10.1016/j.eswa.2013.10.059

    Article  Google Scholar 

  • Bhandari AK, Soni V, Kumar A, Singh GK (2014b) Artificial bee Colony-based satellite image contrast and brightness enhancement technique using DWT-SVD. Int J Remote Sens 35:1601–1624. https://doi.org/10.1080/01431161.2013.876518

    Article  Google Scholar 

  • Bhandari AK, Soni V, Kumar A, Singh GK (2014c) Cuckoo search algorithm based satellite image contrast and brightness enhancement using DWT-SVD. ISA Trans 53:1286–1296. https://doi.org/10.1016/j.isatra.2014.04.007

    Article  Google Scholar 

  • Bhandari AK, Kumar A, Singh GK (2015a) Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur’s, Otsu and Tsallis functions. Expert Syst Appl 42:1573–1601. https://doi.org/10.1016/j.eswa.2014.09.049

    Article  Google Scholar 

  • Bhandari AK, Kumar A, Singh GK (2015b) Tsallis entropy based multilevel thresholding for colored satellite image segmentation using evolutionary algorithms. Expert Syst Appl 42:8707–8730. https://doi.org/10.1016/j.eswa.2015.07.025

    Article  Google Scholar 

  • Bhandari AK, Kumar A, Singh GK, Soni V (2016) Performance study of evolutionary algorithm for different wavelet filters for satellite image denoising using sub-band adaptive threshold. J Exp Theor Artif in℡Ligence 28:71–95. https://doi.org/10.1080/0952813X.2015.1020518

    Article  Google Scholar 

  • Bhandari AK, Kumar A, Chaudhary S, Singh GK (2017) A new beta differential evolution algorithm for edge preserved colored satellite image enhancement. In: Multidimensional systems and signal processing, vol 28. Springer US, pp 495–527. https://doi.org/10.1007/s11045-015-0353-4

    Article  Google Scholar 

  • Campbell JB, Wynne RH (2011) Introduction to remote sensing. Guilford Press

  • Chang, Y. L., Fang, J. P., Benediktsson, J. A., Chang, L., Ren, H., & Chen, K. S. (2009, July). Band selection for hyperspectral images based on parallel particle swarm optimization schemes. In Geoscience and Remote Sensing Symposium, 2009 IEEE International, IGARSS 2009 (Vol. 5, pp. V-84). IEEE

  • Chang YL, Chang L, Xu MX, Chu C (2017) Impurity function band prioritization based on particle swarm optimization and gravitational search algorithm for hyperspectral images. IEEE Geosci Remote Sens (IGARSS):825–829

  • Chen H, Leou J (2009) A new Ikonos imagery fusion approach using particle swarm optimization 2009. Int. Conf. Multimedia and expo. NY, pp 85–88

  • Daamouche A, Melgani F (2009) Swarm intelligence approach to wavelet design for hyperspectral image classification. IEEE Geosci Remote Sens Lett 6:825–829. https://doi.org/10.1109/LGRS.2009.2026191

    Article  Google Scholar 

  • Das, S., Abraham, A., Sarkar, S., 2006. A Hybrid Rough Set--Particle Swarm Algorithm for Image Pixel Classification. 2006 Sixth Int. Conf. Hybrid Intell. Syst. 26–26. https://doi.org/10.1109/HIS.2006.264909

  • Demirel H, Ozcinar C, Anbarjafari G (2009) Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition. IEEE Geosci Remote Sens Lett 7(2):333–337

    Article  Google Scholar 

  • Ding S, Chen L (2009, December) Classification of hyperspectral remote sensing images with support vector machines and particle swarm optimization. In: Int Conf on information engineering and computer science, 2009. ICIECS 2009. IEEE, pp 1–5

  • Dong W, Xiang-bin W (2008) Particle swarm intelligence classification algorithm for remote sensing images. 2008 IEEE Pacific-Asia work. Comput Intell Ind Appl:566–570. https://doi.org/10.1109/PACIIA.2008.26

  • Estes JE, Sailer C, Tinney LR (1986) Applications of artificial intelligence techniques to remote sensing. Prof Geogr 38(2):133–141

    Article  Google Scholar 

  • Gaba P, Singh S, Paliwal KK (2017) Feature selection approach of hyperspectral image using GSA-FODPSO-SVM 2017. Int Conf Comp Comm and Automation (ICCCA), Greater Noida:1070–1075

  • Gajdhane VA, Deshpande LM (2014) Detection of lung cancer stages on CT scan images by using various image processing techniques. IOSR J Computer Engineering (IOSR-JCE) 16(5):28–35

    Article  Google Scholar 

  • Gao J, Gao L, Sun X, Wu Y, Zhang B (2012) GPU implementation of ant colony optimization algorithm for endmember extraction from hyperspectral image 2012. Fourth Workshop Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Shanghai:1–4

  • Ghamisi P, Benediktsson JA (2015) Feature selection based on hybridization of genetic algorithm and particle swarm optimization. Geosci Remote Sens Lett IEEE 12:309–313. https://doi.org/10.1109/LGRS.2014.2337320

    Article  Google Scholar 

  • Ghamisi P, Couceiro MS, Martins FML, Benediktsson JA (2014) Multilevel image segmentation based on fractional-order darwinian particle swarm optimization. IEEE Trans Geosci Remote Sens 52:2382–2394. https://doi.org/10.1109/TGRS.2013.2260552

    Article  Google Scholar 

  • Gharbia R, El Baz AH, Hassanien AE (2016) An adaptive image fusion rule for remote sensing images based on the particle swarm optimization. 2016 Int. Conf. Comput Commun Autom:1080–1085. https://doi.org/10.1109/CCAA.2016.7813903

  • Ghosh A, Datta A, Ghosh S (2013) Self-adaptive differential evolution for feature selection in hyperspectral image data. Appl Soft Comput J 13:1969–1977. https://doi.org/10.1016/j.asoc.2012.11.042

    Article  Google Scholar 

  • Goel S, Sharma A, Goel A (2011) Development of swarm based hybrid algorithm for identification of natural terrain features. Proc - 2011 Int Conf Comput Intell Commun Syst CICN (2011):293–296. https://doi.org/10.1109/CICN.2011.61

  • Golovko V, Bezobrazov S, Kroshchanka A, Sachenko A, Komar M, Karachka A (2017) Convolutional neural network based solar photovoltaic panel detection in satellite photos 2017. Ninth Int. Conf. Intelligent Data Acquisition and Advanced Computing Systems, Technology and Applications (IDAACS). Bucharest, pp 14–19

    Google Scholar 

  • Gupta, S., Arora, A., Panchal, V. K., & Goel, S., 2011. Extended biogeography based optimization for natural terrain feature classification from satellite remote sensing images. Int. Conf. Contemporary Computing (pp. 262–269). Springer, Berlin, Heidelberg

  • Halder A, Ghosh A, Ghosh S (2011) Supervised and unsupervised landuse map generation from remotely sensed images using ant based systems. Appl Soft Comput J 11:5770–5781. https://doi.org/10.1016/j.asoc.2011.02.030

    Article  Google Scholar 

  • Iounousse J, Er-Raki S, El Motassadeq A, Chehouani H (2015) Using an unsupervised approach of probabilistic neural network (PNN) for land use classification from multitemporal satellite images. Appl Soft Comput J 30:1–13. https://doi.org/10.1016/j.asoc.2015.01.037

    Article  Google Scholar 

  • Jain AK (1989) Fundamentals of digital image processing. Prentice Hall, Englewood Cliffs, NJ

    Google Scholar 

  • Jayanth J, Koliwad S, Ashok Kumar T (2015a) Classification of remote sensed data using artificial bee Colony algorithm. Egypt J Remote Sens Sp Sci 18:119–126. https://doi.org/10.1016/j.ejrs.2015.03.001

    Article  Google Scholar 

  • Jayanth J, Kumar, A., Koliwad, S., Krishnashastry, S., 2015b. Artificial bee colony algorithm for classification of remote sensed data. 2015 Int. Conf. Ind. Instrum. Control 1512–1517. 10.1109/IIC.2015.7150989

  • Juneja, M., Walia, E., Sandhu, P. S., & Mohana, R., 2009, July. Implementation and comparative analysis of rough set, artificial neural network (ann) and fuzzy-rough classifiers for satellite image classification. In Intelligent Agent & Multi-Agent Systems, 2009. IAMA 2009. International Conference on (pp. 1–6). IEEE

  • Kumar G, Sarth PP, Ranjan P, Kumar S (2016) Satellite image clustering and optimization using. IEEE Int Conf Power Electron Intell Control Energy Syst:1–4. https://doi.org/10.1109/ICPEICES.2016.7853627

  • Kusetogullari H, Yavariabdi A (2016) Self-adaptive hybrid PSO-GA method for change detection under varying contrast conditions in satellite images. In: Proc. 2016 SAI Comput. Conf. SAI 2016, pp 361–368. https://doi.org/10.1109/SAI.2016.7556007

    Chapter  Google Scholar 

  • Kusetogullari H, Yavariabdi A, Celik T (2015) Unsupervised change detection in multitemporal multispectral satellite images using parallel particle swarm optimization 2015. In: Jour. Of selected topics in applied earth observations and remote Sesing, pp 2151–2164

    Google Scholar 

  • Lee ER, Kim PK, Kim HJ (1994, November) Automatic recognition of a car license plate using color image processing. In: Proceedings of 1st international conference on image processing, vol 2. IEEE, pp 301–305

  • Li L, Chen Y, Yu X, Liu R, Huang C (2015) Sub-pixel flood inundation mapping from multispectral remotely sensed images based on discrete particle swarm optimization. ISPRS J Photogramm Remote Sens 101:10–21. https://doi.org/10.1016/j.isprsjprs.2014.11.006

    Article  Google Scholar 

  • Linyi, L., Deren, L., 2010. Fuzzy Classification of Remote Sensing Images Based on Particle Swarm Optimization. 2010 Int. Conf. Electr. Control Eng. 1039–1042. 10.1109/iCECE.2010.263

  • Liu X, Li X, Liu L, He J, Ai B (2008a) An innovative method to classify remote-sensing images using ant Colony optimization. IEEE Trans Geosci Remote Sens 46:4198–4208. https://doi.org/10.1109/TGRS.2008.2001754

    Article  Google Scholar 

  • Liu X, Li X, Peng X, Li H, He J (2008b) Swarm intelligence for classification of remote sensing data. Sci China Ser D Earth Sci 51:79–87. https://doi.org/10.1007/s11430-007-0133-6

    Article  Google Scholar 

  • Maulik U, Saha I (2009) Modified differential evolution based fuzzy clustering for pixel classification in remote sensing imagery. Pattern Recogn 42:2135–2149. https://doi.org/10.1016/j.patcog.2009.01.011

    Article  Google Scholar 

  • McCorduch P (1979) Machines who think. Freeman Inc, San Francisco

    Google Scholar 

  • McKeown DM (1987) The role of artificial intelligence in the integration of remotely sensed data with geographic information systems. IEEE Trans Geosci Remote Sens GE-25(3):330–348

    Article  Google Scholar 

  • Mohammadzadeh A, Zoej MJV, Tavakoli A (2009) Automatic main road extraction from high resolution satellite imageries by means of particle swarm optimization applied to a fuzzy-based mean calculation approach. J Indian Soc Remote Sens 37:173–184. https://doi.org/10.1007/s12524-009-0021

    Article  Google Scholar 

  • Monteiro, S.T., Kosugi, Y., 2007. A particle swarm optimization-based approach for hyperspectral band selection. 2007 IEEE Congr. Evol. Comput. CEC 2007 3335–3340. https://doi.org/10.1109/CEC.2007.4424902

  • Muangkote, N., Sunat, K., Chiewchanwattana, S., 2016. Multilevel thresholding for satellite image segmentation with moth-flame based optimization. 2016 13th Int. Jt. Conf. Comput Sci Softw Eng JCSSE 2016 0–5. https://doi.org/10.1109/JCSSE.2016.7748919

  • Omkar SN, Mudigere D, Muley D (2007) Urban satellite image classification using biologically inspired techniques 2007. IEEE Int. Symp. ISIE

  • Paoli, A., Melgani, F., Pasolli, E., 2009. Swarm intelligence for unsupervised classification of hyperspectral images. Geosci. Remote Sens. Symp. IEEE Int. 2009 5, V-96-V-99. 10.1109/IGARSS.2009.5417723

  • Papa JP, Fonseca LMG, De Carvalho LAS (2010) Projections onto convex sets through particle swarm optimization and its application for remote sensing image restoration. Pattern Recogn Lett 31:1876–1886. https://doi.org/10.1016/j.patrec.2010.02.012

    Article  Google Scholar 

  • Praveena, S., Singh, S.P., 2014. Hybrid clustering algorithm and feed-forward neural network for satellite image classification 2015 Int Conf Industrial Instrumentation and Control (ICIC), Pune pp. 1378–1383

  • Sachdeva S, Bhatia T, Verma AK (2017) Flood susceptibility mapping using GIS-based support vector machine and particle swarm optimization: a case study in Uttarakhand (India). 8th Int. Conf. Comput Commun Netw Technol ICCCNT 2017. https://doi.org/10.1109/ICCCNT.2017.8204182

  • Samadzadegan, F., Mahmoudi, F.T., 2011. Optimum band selection in hyperspectral imagery using swarm intelligence optimization algorithms. 2011 Int. Conf. Image Inf. Process. 1–6. 10.1109/ICIIP.2011.6108925

  • Sarkar S, Das S, Chaudhuri SS (2016) Hyper-spectral image segmentation using Rényi entropy based multi-level thresholding aided with differential evolution. Expert Syst Appl 50:120–129. https://doi.org/10.1016/j.eswa.2015.11.016

    Article  Google Scholar 

  • Senthilnath J, Omkar SN, Mudigere D, Kumar MM (2008) Crop Classifi cation using biologically-inspired techniques with high resolution satellite image 2008. Jour. Indian Society of Remote Sensing:175–182

  • Senthilnath, J., Omkar, S.N., Mani, V., Karthikeyan, T., 2011a. Multi-objective optimization of satellite image registration using Discrete Particle Swarm Optimisation. 2011 Annu. IEEE India Conf. 1–5. https://doi.org/10.1109/INDCON.2011.6139338

  • Senthilnath, J., Omkar, S.N., Mani, V., Tejovanth, N., Diwakar, P.G., B, A.S., 2011b. Multi-spectral satellite image classification using glowworm swarm optimization 2011. IEEE Int Geo and Remote Sesing Symp Vancouver. pp. 47–50

  • Senthilnath J, Omkar SN, Mani V, Tejovanth N, Diwakar PG, Archana Shenoy B (2012) Hierarchical clustering algorithm for land cover mapping using satellite images. IEEE J Sel Top Appl Earth Obs Remote Sens 5:762–768. https://doi.org/10.1109/JSTARS.2012.2187432

    Article  Google Scholar 

  • Senthilnath J, Shenoy HV, Omkar SN, Mani V (2013) Spectral-spatial MODIS image analysis using swarm intelligence algorithms and region-based segmentation for flood assessment. In: Int. Conf. Bio-inspired computing: theories and applications (BIC-TA 2012). Springer, India, pp 163–174

    Google Scholar 

  • Senthilnath J, Omkar SN, Mani V, Prasad R, Rajendra R, Shreyas PB (2015) Multi-sensor satellite remote sensing images for flood assessment using swarm intelligence. Int Conf Cogn Comput Inf Process (CCIP) Noida India:1–5

  • Senthilnath J, Kulkarni S, Benediktsson JA, Yang XS (2016) A novel approach for multispectral satellite image classification based on the bat algorithm 2016. IEEE Geosci Remote Sens Lett 13:599–603. https://doi.org/10.1109/LGRS.2016.2530724

    Article  Google Scholar 

  • Shahana K, Ghosh S, Jeganathan C (2016, April) A survey of particle swarm optimization and random forest based land cover classification. In: Computing, communication and automation (ICCCA), 2016 international conference on. IEEE, pp 241–245

  • Singh V, Kumar G, Arora G (2016) Analytical evaluation for the enhancement of satellite images using swarm intelligence techniques. In computing for sustainable global development (INDIACom). 2016 3rd Int. Conf on IEEE 16:2401–2405

    Google Scholar 

  • Singh A, Chhablani C, Goel L (2017) Moth flame optimization for land cover feature extraction in remote sensing images.2017. Eighth. Int Conf on Comp Comm Net Tech (ICCCNT):1–7

  • Soliman, O.S., Mahmoud, A.S., Hassan, S.M., 2012. Remote Sensing Satellite Images Classification Using Support Vector Machine and Particle Swarm Optimization. 2012 Third Int. Conf. Innov. Bio-Inspired Comput. Appl. 280–285. 10.1109/IBICA.2012.61

  • Sood, M., Menon, S., 2016 Cross-country path finding algorithm using hybridization of Bat and cuckoo search. 2016 Third Int. Conf. Computing for Sustainable Global Development (INDIACom), New Delhi

  • Suresh S, Lal S (2016) An efficient cuckoo search algorithm based multilevel thresholding for segmentation of satellite images using different objective functions. Expert Syst Appl 58:184–209. https://doi.org/10.1016/j.eswa.2016.03.032

    Article  Google Scholar 

  • Tebbi MA, Haddad B (2017) Improving infrared MSG satellite images classification, application on rainy areas detection. Proc. 2016 8th Int. Conf Model Identif Control ICMIC (2016):747–750. https://doi.org/10.1109/ICMIC.2016.7804211

  • Tien Bui D, Bui QT, Nguyen QP, Pradhan B, Nampak H, Trinh PT (2017) A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Agric For Meteorol 233:32–44. https://doi.org/10.1016/j.agrformet.2016.11.002

    Article  Google Scholar 

  • Turner BL, Meyer WB, Skole DL (1994) Global land-use/land-cover change: towards an integrated study. Ambio Stockholm 23(1):91–95

    Google Scholar 

  • Upadhyay, A., Singh, S.K., Singh, P., Singh, P., 2010. Comparative study of artificial neural network based classification of 1RS LISS-III satellite images 961–965. https://doi.org/10.1109/ICGCIoT.2015.7380601

  • Wang Q, Wang L, Liu D (2012) Particle swarm optimization-based sub-pixel mapping for remote-sensing imagery. Int J Remote Sens 33:6480–6496. https://doi.org/10.1080/01431161.2012.690541

    Article  Google Scholar 

  • Wang L, Geng H, Liu P, Lu K, Kolodziej J, Ranjan R, Zomaya AY (2015) Particle swarm optimization based dictionary learning for remote sensing big data. Knowledge-Based Syst 79:43–50. https://doi.org/10.1016/j.knosys.2014.10.004

    Article  Google Scholar 

  • Xue Z, Du P, Su H (2014) Harmonic analysis for hyperspectral image classification integrated with PSO optimized SVM. IEEE J Sel Top Appl Earth Obs Remote Sens 7:2131–2146. https://doi.org/10.1109/JSTARS.2014.2307091

    Article  Google Scholar 

  • Yamaguchi T, Mori K, Mackin KJ, Nagai Y (2012) Application of particle swarm optimization to similar image search on satellite sensor data. 6th Int. Conf Soft Comput Intell Syst 13th Int Symp Adv Intell Syst:1573–1577. https://doi.org/10.1109/SCIS-ISIS.2012.6505327

  • Yang L, Sun X, Peng L, Yao X, Chi T (2015) An agent-based artificial bee Colony (ABC) algorithm for hyperspectral image endmember extraction in parallel. IEEE J Sel Top Appl Earth Obs Remote Sens 8:4657–4664. https://doi.org/10.1109/JSTARS.2015.2454518

    Article  Google Scholar 

  • Yavari S, Valadan Zoej MJ, Mohammadzadeh A, Mokhtarzade M (2013) Particle swarm optimization of RFM for georeferencing of satellite images. IEEE Geosci Remote Sens Lett 10:135–139. https://doi.org/10.1109/LGRS.2012.2195153

    Article  Google Scholar 

  • Zaitoun NM, Aqel MJ (2015) Survey on image segmentation techniques. Procedia Computer Science 65:797–806

    Article  Google Scholar 

  • Zarrinpanjeh N, Samadzadegan F, Schenk T (2013) A new ant based distributed framework for urban road map updating from high resolution satellite imagery. Comput Geosci 54:337–350. https://doi.org/10.1016/j.cageo.2012.12.006

    Article  Google Scholar 

  • Zhang B, Sun X, Gao L, Yang L (2011a) Endmember extraction of hyperspectral remote sensing images based on the ant Colony optimization (ACO) algorithm. IEEE Trans Geosci Remote Sens 49:2635–2646. https://doi.org/10.1109/TGRS.2011.2108305

    Article  Google Scholar 

  • Zhang B, Sun X, Gao L, Yang L (2011b) Endmember extraction of hyperspectral remote sensing images based on the discrete particle swarm optimization algorithm. IEEE Trans Geosci Remote Sens 49:4173–4176. https://doi.org/10.1109/TGRS.2011.2131145

    Article  Google Scholar 

  • Zhang B, Gao J, Gao L, Sun X (2013) Improvements in the ant colony optimization algorithm for endmember extraction from hyperspectral images. IEEE J Selected Topics in Applied Earth Observations and Remote Sensing 6(2):522–530

    Article  Google Scholar 

  • Zhang, B., Gao, L., Sun, X., Zhuang, L., 2017. Swarm intelligence: a reliable solution for extracting endmembers from hyperspectral imagery. Work Hyperspectral Image Signal Process Evol Remote Sens 2015–June. https://doi.org/10.1109/WHISPERS.2015.8075433

  • Zhong Y, Zhang L, Huang B, Li P (2006) An unsupervised artificial immune classifier for multi/hyperspectral remote sensing imagery. IEEE Trans Geosci Remote Sens 44:420–431. https://doi.org/10.1109/TGRS.2005.861548

    Article  Google Scholar 

  • Zhong Y, Zhao L, Zhang L (2014) An adaptive differential evolution endmember extraction algorithm for hyperspectral remote sensing imagery. IEEE Geosci Remote Sens Lett 11:1061–1065. https://doi.org/10.1109/LGRS.2013.2285476

    Article  Google Scholar 

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  1. Amity University Uttar Pradesh, Noida, Uttar Pradesh, India

    Snehlata Sheoran & Neetu Mittal

  2. Instituto Politécnico Nacional (IPN), Mexico, Mexico

    Alexander Gelbukh

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

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