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Computational efficient compression scheme for satellite images

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Abstract

Satellite image transmission and storage have limitations like limited bandwidth to send the signals to limited number of receiving stations and a huge storage requirements for ultra-resolution photographs. Above limitations and requirements calls for efficient compression algorithms which are least complex and compress the images without affecting the Region of Interest (ROI). This work we propose a compression scheme where a Sigmoidal activation function using orthogonal projection based ELM for shape adaptive compression of water body satellite images. The ROI is determined using saliency maps. Proposed method efficiently compresses the satellite images with adaptive compression with a better PSNR, structural content and normalized errors.

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References

  • Achanta R, Süsstrunk S (2009) Saliency detection for content-aware image resizing. 2009 16th IEEE International Conference on Image Processing (ICIP).Date of Conference: 7–10 Nov. 2009. https://doi.org/10.1109/ICIP.2009.5413815

  • Anasuodei M, Friday Eleonu O (2021) An Enhanced Satellite Image Compression Using Hybrid (DWT, DCT and SVD) Algorithm. Am J Comput Sci Technol 4(1):1–10

    Article  Google Scholar 

  • Baeza I, Verdoya J-A, Villanueva-Oller J, Villanuevaa R-J (2009) ROI-based procedures for progressive transmission of digital images: A comparison. Math Comput Model 50(5–6):849–859

    Article  Google Scholar 

  • Chen H, Luo H, Yu F-X, Huang Z-L, Liu J-X (2010) Progressive Satellite Image Transmission Based on Integer Discrete Cosine Transform . Inf Technol J 9: 169–173. https://scialert.net/abstract/?doi=itj.2010.169.173. https://doi.org/10.3923/itj.2010.169.173

  • Delaunay X, Chabert M, Charvillat V, Morin G, Ruiloba R (2008) Satellite image compression by directional decorrelation of wavelet coefficients. 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. Date of Conference: 31 March-4 April 2008. https://doi.org/10.1109/ICASSP.2008.4517829 .

  • Gangadhar DB, Ananth AG (2018) Satellite Image Compression Using DCT Technique. 2018 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT). Date of Conference: 14–15. https://doi.org/10.1109/ICEECCOT43722.2018.9001533

  • Giannopoulos M, Aidini A, Pentari A, Fotiadou K, Tsakalides P (2020) Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks. J Imaging 6(4):24. https://doi.org/10.3390/jimaging6040024

    Article  Google Scholar 

  • Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew. Extreme learning machine: Theory and applications .Neurocomputing 70 (2006) 489–501.

  • Hagag A, Hassan ES, Fathi MA, Abd El-Samie E, Fan X (2017) Satellite multispectral image compression based on removing sub-bands. Optik 131:1023–1035. https://doi.org/10.1016/j.ijleo.2016.11.172

    Article  Google Scholar 

  • Heltin Genitha C, Kavin Rajesh R (2016) A technique for multi-spectral satellite image compression using EZW algorithm. 2016 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) .Date of Conference: 16–17 Dec. 2016. https://doi.org/10.1109/ICCICCT.2016.7988040

  • Jagersand M (1995) Saliency maps and attention selection in scale and spatial coordinates: an information theoretic approach. Proc IEEE Int Conf Comput V. https://doi.org/10.1109/ICCV.1995.466786

  • Jamunarani M, Vasanthanayaki C (2019) Shape adaptive DCT compression for high quality surveillance using wireless sensor networks. Cluster Comput 22:3737–3747. https://doi.org/10.1007/s10586-018-2249-1

  • Jamuna Rani M, Vasanthanayaki C (2020) Network Condition Based Multi-Level Image Compression and Image Transmission in Wireless Sensor Networks. J Cluster Comput-J Comput Commun 150:317–32

    Article  Google Scholar 

  • Kadhim QK (2016) Image Compression Using Discrete Cosine Transform Method. Int J Comput Sci Mob Comput IJCSMC 5(9):186–192

    Google Scholar 

  • Khalaf OI, Tavera Romero CA, Azhagu Jaisudhan Pazhani A, Vinuja G (2021) VLSI Implementation of a High-Performance Nonlinear Image Scaling Algorithm. J Healthcare Eng. 2021, Article ID 6297856, 10

  • Li Jin, Liu Zilong (2019) Multispectral Transforms Using Convolution Neural Networks for Remote Sensing Multispectral Image Compression. Convolutional Neural Networks Applications in Remote Sensing. Remote Sens. 11(7):759. https://doi.org/10.3390/rs11070759

    Article  Google Scholar 

  • Liu X, Hu Q, Cai Y, Cai Z (2020) Extreme Learning Machine-Based Ensemble Transfer Learning for Hyperspectral Image Classification. IEEE J Sel Top Appl Earth Obs Remote Sens. 13:3892-3902. https://doi.org/10.1109/JSTARS.2020.3006879

  • Mukherjee P, Lall B, Shah A (2015) Saliency map based improved segmentation. 2015 IEEE International Conference on Image Processing (ICIP).Date of Conference: 27–30 Sept. 2015. https://doi.org/10.1109/ICIP.2015.7351008

  • Oleiwi ZC, Al-Shammary D, Al-Asfoor M, Ibaida A (2021) Light network high performance discrete cosine transform for digital images. Vis Inform 5(2):41–50

    Article  Google Scholar 

  • Patra A, Saha A, Chakraborty D, Bhattacharya K (2021) Compression of high-resolution satellite images using optical image processing. https://doi.org/10.5772/intechopen.94147

  • Pazhani AAJ (2022) Computation unit architecture for satellite image processing systems. Earth Sci Inform 15(1):185–195. https://doi.org/10.1007/s12145-021-00715-1

  • Pazhani AAJ, Vasanthanayaki C (2022) Object detection in satellite images by faster R-CNN incorporated with enhanced ROI pooling (FrRNet-ERoI) framework. Earth Sci Inform 15:553–561. https://doi.org/10.1007/s12145-021-00746-8

  • Pazhani AAJ, Samuel TSA (2022) High-Speed and Area-Efficient Modified Binary Divider. Circ Syst Signal Process 41:3350–3371

    Article  Google Scholar 

  • Ryan MJ, Arnold JF (1997) The lossless compression of AVIRIS images by vector quantization. IEEE Trans Geosci Remote Sens 35(3):546–550. https://doi.org/10.1109/36.581964

    Article  Google Scholar 

  • Sahnoun K, Benabadji N (2013) Color satellite image compression using the evidence theory and Huffman coding. 2013 World Congress on Computer and Information Technology (WCCIT) . Date of Conference: 22–24 June 2013. https://doi.org/10.1109/WCCIT.2013.6618755.

  • Susilo RM, Bretschneider TR (2003) On the realtime satellite image compression of X-Sat, Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint. Date of Conference: 15–18 Dec. 2003. https://doi.org/10.1109/ICICS.2003.1292497

  • Swetha V, Premjyoti Patil G, Shantakumar Patil B (2021) Lossless Compression of Satellite Images using a Versatile Hybrid Algorithm. IOP Conf. Series: Mater Sci Eng 1166: 012048 . IOP Publishing. https://doi.org/10.1088/1757-899X/1166/1/012048

  • Zhao Q, Hu Y, Cao J (2009) Automatic image segmentation based on saliency maps and Fuzzy SVM. IET International Communication Conference on Wireless Mobile and Computing (CCWMC 2009) . Date of Conference: 7–9 Dec. 2009

  • Zhu H, Wang YG (2013) A modified extreme learning machine with sigmoidal activation functions. Neural Comput Appl. https://doi.org/10.1007/s00521-012-0860-2

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Authors and Affiliations

  1. Department of ECE, Sona College of Technology, Salem, Tamilnadu, India

    M. Jamuna Rani

  2. Department of ECE, Ramco Institute of Technology, Rajapalayam, Tamilnadu, India

    A. Azhagu Jaisudhan Pazhani

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  1. M. Jamuna Rani
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  2. A. Azhagu Jaisudhan Pazhani
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Correspondence to M. Jamuna Rani.

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Communicated by: H. Babaie

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Jamuna Rani, M., Azhagu Jaisudhan Pazhani, A. Computational efficient compression scheme for satellite images. Earth Sci Inform 15, 1723–1736 (2022). https://doi.org/10.1007/s12145-022-00831-6

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