Abstract
Synthetic aperture radar (SAR) images are difficult to analyze due to the presence of speckle noise. Speckle noise must be filtered out before applying to other image processing applications. Three-layered feed forward back propagation neural network (TLFFBPNN) has been proposed to suppress the speckle noise. Gray-level co-occurrence matrix properties have been extracted, and back propagation training algorithm is used to train the neural network. The performance metrics such as peak signal to noise (PSNR), structural similarity index matrix (SSIM), edge preservation index (EPI), equivalent number of looks (ENL), and speckle suppression index (SSI) have been evaluated to find the efficiency of TLFFBPNN and compared with four recently developed de-speckling techniques. The exploratory outcomes show that the TLFFBPNN method has better de-speckling execution with great edge preservation. The comparative outcome reveals that the proposed TLFFBPNN de-speckled method outperformed in terms of PSNR of 0.98%, SSIM of 1.0%, SSI of 2.0%, EPI of 0.84%, and ENL of 0.5% when compared with the Wiener Filter Sparse Optimization in Contourlet transform domain de-speckling method.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abdullah HN, Hasan MF, Tawfeeq QS (2008) Speckle noise reduction in SAR images using double-density dual tree DWT. Asian J Inform Technol 7(7):281–284
Amirmazlaghani M, Amindavar H (2012) A novel sparse method for despeckling SAR images. IEEE Trans Geosci Remote Sens 50(12):5024–5032
Arabfard M, Maghsoudloo M, Rafati M, Zadeh MRR (2016) Assessment of noise reduction in ultrasound images of common carotid and brachial arteries. IET Comput Vis 10(1):1–8
Bhuiyan MIH, Ahmad MO, Swamy MNS (2007) Spatially adaptive wavelet-based method using the Cauchy prior for de-noising the SAR images. IEEE Trans Circ Syst Video Technol 7(4):500–507
Chen G, Liu X (2005) Wavelet-based de-speckling SAR images using neighboring wavelet coefficients. IEEE Int Geosci Remote Sens Symp IGARSS'05 3:1764–1766
Choi H, Jeong J (2018) Despeckling images using a preprocessing filter and discrete wavelet transform-based noise reduction techniques. IEEE Sensors J 18(8):3131–3139
Donoho DL (1994) Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3):425–455
Fang Qiu Richardson I, Jensen JR (2004) Speckle noise reduction in SAR imagery using a local adaptive median filter. Geosci Remote Sens 41(3):244–266
Feng WS, Lei H (2014) SAR image despeckling using data-driven tight frame. IEEE Geosci Remote Sens Lett 11(9):1455–1459
Ji XX, Zhang G (2013) An improved orthogonal matching pursuit algorithm for blind sparsity signal reconstruction. J Astronaut 8:146–1151
Ji X, Zhang G (2015) Contourlet domain SAR image de-speckling via self-snake diffusion and sparse representation. Multimed Tools Appl 76(4):5873–5887
Kalaiyarasi M, Saravanan S, Perumal B (2016) A survey on: de-speckling methods of SAR image. International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT)
Le TT, Atto AM, Trouve E, Nicolas J-M (2014) Adaptive multi temporal SAR image filtering based on the change detection matrix. IEEE Geosci Remote Sens Lett 11(10):1826–1830
Loizou CP, Theophanous C, Pantziaris M, Kasparis T (2014) De-speckle filtering software toolbox for ultrasound imaging of the common carotid artery. Comput Methods Prog Biomed 114(1):109–124
Nadernejad E, Hassanpour H (2007) A comparison and analysis of different PDE-based approaches for image enhancement. International Conference on Signal Processing and Communication Systems (ICSPCS)
Nadernejad E, Koohi H, Hassanpour H (2008) PDEs-based method for image enhancement. Appl Math Sci 2(20):981–993
Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639
Shanthi I, Valarmathi ML (2013) SAR image despeckling using Possibilistic fuzzy C-means clustering and edge detection in Bandlet domain. Neural Comput Applic 23(1):279–291
Silvana S, Dellepiane EA (2014) Quality assessment of despeckled SAR images. IEEE J Sel Top Appl Earth Observ Remote Sens 7(2):691–707
Sridevi S, Nirmala S (2016) Fuzzy inference rule based image despeckling using adaptive maximum likelihood estimation. J Intell Fuzzy Syst 31(1):433–441
Xu B, Cui Y, Li Z, Yang J, Song J (2015) Patch ordering-based SAR image de-speckling via transform domain filtering. IEEE J Sel Top Appl Earth Observ Remote Sens 8(4):1682–1695
Zhang W, Liu F, Jiao L, Hou B, Wang S, Shang R (2010) SAR image despeckling using edge detection and feature clustering in bandlet domain. IEEE Geosci Remote Sens Lett 7(1):131–135
Zhao Y, Liu JG, Zhang B, Hong W, Wu R (2015) Adaptive total variation regularization based SAR image despeckling and despeckling evaluation index. IEEE Trans Geosci Remote Sens 53(5):2765–2774
Acknowledgments
We thank the Department of ECE of Kalasalingam Academy of Research and Education (deemed to be University), Tamil Nadu, India for the computational facilities made available to us in Centre for Signal Processing Laboratory (supported by Department of Science and Technology (DST), New Delhi under FIST Program) (Reference No: SR/FST/ETI-336/2013 dated November 2013).
Author information
Authors and Affiliations
Corresponding author
Additional information
Responsible Editor: Abdullah M. Al-Amri
Rights and permissions
About this article
Cite this article
Murugesan, K., Balasubramani, P. & Murugan, P.R. A quantitative assessment of speckle noise reduction in SAR images using TLFFBP neural network. Arab J Geosci 13, 35 (2020). https://doi.org/10.1007/s12517-019-4900-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-019-4900-4