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Classification of LISS-III Image Using Fuzzy Logic

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Advances in Information Communication Technology and Computing

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 135))

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Abstract

The LISS-III is the multi-phantom camera working in four groups. The main reason behind accompanying the work is to apply calculation dependent on regulated characterization of systems to comprehend the land spread and land utilized region in Mumbai. Here, we have used the IRS P6 LISS-III satellite picture of Mumbai locale is utilized to group the regions of Mumbai, Navi Mumbai, and Thane district. The classifier utilized is a fuzzy inference system and band pictures. The various regions of Mumbai locale are grouped, for example, zone secured by mangroves, forest, water, and developed area. It is been seen that the accuracy of fuzzy inference system is 77.88%.

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References

  1. Mahashwari T, Asthana A (2013) Image enhancement using fuzzy technique. IJRREST 2(2). ISSN 2278-6643

    Google Scholar 

  2. Janhavi Shirke NMS (2016) Multi-label classification of a scene image using fuzzy logic. Int J Comput Appl (0975–8887) Emerg Trends in Comput

    Google Scholar 

  3. Younes AA, MSH T, Akdag H (2005) Color image profiling using fuzzy sets. Turk J Elec Engin 13(3)

    Google Scholar 

  4. Priyadharshini M, Karthi R, Sangeethaa SN, Premalatha R, Tamilselvan KS (2013) Implementation of fuzzy logic for the high-resolution remote sensing images with improved accuracy. IOSR J Electr Electron Eng (IOSR-JEEE) 5(3):13. e-ISSN: 2278-1676, p-ISSN:2320-3331

    Google Scholar 

  5. Souverville S, Rosales JA, Gallegos FJ, Dehesa M, Hernández IV, Lozano LV (2015) Fuzzy logic applied to improvement of image resolution using gaussian membership functions. Res Comput Sci 102:77–88 (rec. 2015-03-28; acc.2015-07-15)

    Google Scholar 

  6. Mustafa NBA, Khaleel Ahmed S, Ali Z, Yit WB, Abidin AAZ, Md Sharrif ZA (2009) Agricultural produce sorting and grading using support vector machines and fuzzy logic. In: 2009 IEEE international conference on signal and image processing applications, pp. 391–396

    Google Scholar 

  7. Sharma M, Gupta R, Kumar D, Kapoor R (2011) Efficacious approach for satellite image classification. J. Electr Electron Eng Res 3(8):143–150. ISSN: 2141–2367 (©2011 Academic Journals)

    Google Scholar 

  8. Park V, Lee H-K (1998) Fuzzy logic based satellite image classification: generation of fuzzy membership function and rule from training set. IAPR workshop on machine vision applications. Nov. 17–19. 1998, Makuhari, Chiba Japan

    Google Scholar 

  9. Kamra A, Rani K (2012) An improved method for image enhancement using fuzzy approach. IRACST (IJCSITS) 2(6). ISSN: 2249–9555

    Google Scholar 

  10. Kaur A, Kaur A (2012) Comparison of mamdani-type and sugeno-type fuzzy inference systems for air conditioning system. IJSCE 2(2)

    Google Scholar 

  11. Shenbagavalli R, Ramar K (2013) Satellite image edge detection using fuzzy logic. Int J Eng Sci (IJES) 2(1): 47–52. ISSN: 2319–1813 ISBN: 2319–1805.

    Google Scholar 

  12. Naganur, HG, Sannakki HG, Rajpurohit, VS Arunkumar R (2012) Fruits sorting and grading using fuzzy logic. Int J Adv Res Comput Eng Technol (IJARCET) 1(6). ISSN: 2278--1323

    Google Scholar 

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Correspondence to Anand Upadhyay .

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Upadhyay, A., Mishra, S., Khavadkar, A. (2021). Classification of LISS-III Image Using Fuzzy Logic. In: Goar, V., Kuri, M., Kumar, R., Senjyu, T. (eds) Advances in Information Communication Technology and Computing. Lecture Notes in Networks and Systems, vol 135. Springer, Singapore. https://doi.org/10.1007/978-981-15-5421-6_29

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  • DOI: https://doi.org/10.1007/978-981-15-5421-6_29

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5420-9

  • Online ISBN: 978-981-15-5421-6

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