Hyperspectral Image Compression and Classification: A Survey

Open Access

Year : 2022 | Volume : | Issue : 1 | Page : 9-21
By

    B. Sucharitha

  1. Nida Zia

  2. Tahereen Rizvi

  3. Syed Diraar Ahmed

  1. Assistant Professor, Department of Electronics and Communication Engineering, Muffakham Jah College of Engineering and Technology, Hyderabad, India
  2. Student, Department of Electronics and Communication Engineering, Muffakham Jah College of Engineering and Technology, Hyderabad, India
  3. Student, Department of Electronics and Communication Engineering, Muffakham Jah College of Engineering and Technology, Hyderabad, India
  4. Student, Department of Electronics and Communication Engineering, Muffakham Jah College of Engineering and Technology, Hyderabad, India

Abstract

The applications of hyperspectral images (HSI) are many, which include agriculture, food quality, remote sensing, medical diagnostics and safety assessment. Hyperspectral image analysis has been used for detecting contaminants and identifying defects in food. It also utilizes advanced software and hardware tools hence allowing users to diagnose and detect pathologies. In this paper an avant-garde investigation about Hyperspectral image compression and classification techniques which can be used in various applications like broadcasting of television, remote sensing via satellite, storage and classification of medical images, pictures and documents has been made. Significant increase in multimedia products has created a need to enhance, extract, store and interpret the information received in the most effective manner. The size of a Hyperspectral image comprises approximately 138.81 megabytes and hence requires large space for storage. Hence, Hyperspectral image compression is of great importance as it reduces the data redundancy and the hardware space required for storage. Hyperspectral image classification has gained great research attention due to the increasing demand of feature information extraction. This survey focuses on describing the recent advances in spectral spatial classification of hyperspectral images and various recent advancements in compression techniques for input HSIs.

Keywords: Hyperspectral image processing, compression, classification, remote sensing

[This article belongs to Journal of Remote Sensing & GIS(jorsg)]

How to cite this article: B. Sucharitha, Nida Zia, Tahereen Rizvi, Syed Diraar Ahmed Hyperspectral Image Compression and Classification: A Survey jorsg 2022; 13:9-21
How to cite this URL: B. Sucharitha, Nida Zia, Tahereen Rizvi, Syed Diraar Ahmed Hyperspectral Image Compression and Classification: A Survey jorsg 2022 {cited 2022 May 23};13:9-21. Available from: https://journals.stmjournals.com/jorsg/article=2022/view=91772

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References

1. Gogineni R, Chaturvedi A. Hyperspectral Image Classification. Processing and Analysis of Hyperspectral Data. Book Chapter. Edited. Volume. 2020. doi: 10.5772/intechopen.88925.
2. Khan MJ, Khan HS, Yousaf A, Khurshid K, Abbas A. Modern trends in hyperspectral image analysis: a review. IEEE Access. 2018;6:14118–29. doi: 10.1109/ACCESS.2018.2812999.
3. Sucharitha B, Anithasheela K. Hybrid compression method for hyper spectral images using singular value decomposition and discrete wavelet transform. IEEE International Conference on Intelligent Systems, Smart and Green Technologies (ICISSGT). Nov 13–14 2021; Visakhapatnam, India, US: IEEE Press. Vol. 2022; 2021. doi: 10.1109/ICISSGT52025.2021.00032.
4. Fauvel M, Tarabalka Y, Benediktsson JA, Chanussot J, Tilton JC. Advances in spectral-spatial classification of hyperspectral images. Proc IEEE. Proceedings of the IEEE. 2013, 101 (3):652–75. doi: 10.1109/JPROC.2012.2197589.
5. Sucharitha B, Ather S. Hyper spectral image compression using fractal compression with arithmetic and Huffman coding, International Journal of Emerging Technologies and Innovative Research. June 2019;6(6):90–5.
6. Ugur Toreyin YB, Yılmaz O, Mert YM, Turk F. Lossless hyperspectral image compression using wavelet transform based spectral decorrelation 7th International Conference on Recent Advances in Space Technologies (RAST). Jun 16–19 2015; Istanbul, Turkey. Vol. 2015. IEEE Publications; 2015. p. 251–4.
7. Xue Jize, Zhao Y, Liao W, Chan JC-W. Nonlocal tensor sparse representation and low-rank regularization for hyperspectral image compressive sensing reconstruction. Remote Sens. 2019;11(2):193. doi: 10.3390/rs11020193.
8. Guerra R, Mar ́, Íaz ́D, Barrios Y, Ópez SL, Sarmiento R. A Hardware-Friendly Algorithm for the on-board Compression of hyperspectral Images 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). Sep 23–26 2018; Amsterdam, Netherlands, US. IEEE Publications; 2018. p. 2019.
9. Karami A, Yazdi M, Mercier G. Compression of Hyperspectral Images Using Discrete Wavelet Transform and Tucker Decomposition. IEEE J Sel Top Appl Earth Observations Remote Sensing. April 2012;5(2):444–50. doi: 10.1109/JSTARS.2012.2189200.
10. Masoodhu Banu NM, Sujatha S Et al. Sakib Khan Pathan. Skip Block Based Distrib Source Coding Hyperspectr Image Compression. Multimedia Tools & Applications. 2016;75(18):11267–89.
11. Dua Y, Kumar V, Singh RS. Comprehensive Review of hyperspectral image compression algorithms. Opt Eng. September 2020;59(9). doi: 10.1117/1.OE.59.9.090902.
12. Gunasheela KS, Prasantha HS. Compressive sensing approach to hyperspectral image compression. ICTACT J Image Video Process. August 2018;9(1):1849–56. doi: 10.21917/ijivp.2018.0261.
13. Fu C, Yi Y, Luo F. Hyperspectral image compression based on simultaneous sparse representation and general-pixels. Pattern Recognit Lett. December 2018;116(1):65–71. doi: 10.1016/j.patrec.2018.09.013.
14. Mei S, Khan BM, Zhang Y, Du Q. Low-Complexity hyperspectral Image Compression using folded PCA and JPEG2000. IGARSS 2018- IEEE International Geoscience and Remote Sensing Symposium. July 22–27 2018. Valencia, Spain, US: IEEE Publications.
15. Mamatha AS, Singh V. Lossless hyperspectral image compression based on prediction. 2013 IEEE recent advances in intelligent computational systems (RAICS). 19–21 December 2013. Trivandrum, India, US: IEEE Publications; 2014.
16. Yadav RJ, Nagmode MS. Compression of hyperspectral image using PCA–DCT technology. Lecture Notes in Networks and Systems. 2018;7:269–77. doi: 10.1007/978–981–10–3812–9_28.
17. Guo Y, Han S, Cao H, Zhang Y, Wang Q. Guided filter based Deep Recurrent Neural Networks for hyperspectral Image Classification. Procedia Comput Sci. 2018;129:219–23. doi: 10.1016/j.procs.2018.03.048.
18. Cao Xiangyong, Zhou F, Xu L, Meng D, Xu Z, Paisley J. Hyperspectral image classification with Markov random fields and a convolutional neural network. IEEE Trans Image Process. May 2018;27(5):2354–67. doi: 10.1109/TIP.2018.2799324, PMID 29470171.
19. Tarabalka Y, Chanussot J, Benediktsson JA. Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recognit. 2010;43(7):2367–79. doi: 10.1016/j.patcog.2010.01.016.
20. Beirami BA, Mokhtarzade M. Spatial-spectral classification of hyperspectral images based on multiple fractal-based features. Geocarto Int. 2022;37(1):231–45. doi: 10.1080/10106049.2020.1713232.
21. Hasan H, Shafri HZM, Habshi M. A comparison between support vector machine (SVM) and convolutional neural network (CNN) models for hyperspectral image classification. IOP Conf Ser.: Earth Environ Sci. 2019;357(1). doi: 10.1088/1755–1315/357/1/012035.
22. Li Y, Li Junbao, Pan J-S. Hyperspectral image recognition using SVM combined deep learning. J Internet Technol. 2019;20:851–9.
23. Archibald R, Fann G. Feature selection and classification of hyperspectral images with support vector machines. IEEE Geosci Remote Sensing Lett. October 2007;4(4):674–7. doi: 10.1109/LGRS.2007.905116.
24. Liu B, Yu X, Yu A, Zhang P, Wan G. Spectral-spatial classification of hyperspectral imagery based on recurrent neural networks. Remote Sens Lett. 2018;9(12):1118–27. doi: 10.1080/2150704X.2018.1511933.


Regular Issue Open Access Article
Volume 13
Issue 1
Received April 25, 2022
Accepted May 9, 2022
Published May 23, 2022