Wavelet Transform Technique Applied to Satellite Image Denoising

Source:
By:Ibrahim Goni, Asabe Sandra Ahmadu, Yusuf Musa Malgwi

DOI: https://doi.org/10.30564/ese.v5i1.5235

Abstract:

Satellite images either digital or analog must have certain elements that are accidentally introduced during the processing of capturing as a result of weather or system sensor known as electronic noise. However, several attempts and advances have been made by academicians, industries and intelligent security agencies to remove this noise. It has been a nagging problem in the area of computer vision, image processing and artificial intelligence to denoise satellite images and noise removal is among the significant components in satellite image analysis. The aim of this research work was to denoise the satellite image of Sambisa forest using the wavelet transform technique. Satellite images of Sambisa forest captured by Landsat satellite in 2007, 2013, 2014, 2019 and 2021 respectively with their associated Geo-referenced 11.2503° N Longitude and 13.4167° E Latitude were downloaded from the United States Geological Survey (USGS) website. The images are acquired as Zipped Geo-referenced Tagged Image File Format (GeoTIFF). Color Composite bands of natural colors (bands 2, 3 and 4) are combined using the ArcGIS software and RGB image were obtained. Wavelet transforms denoising technique was used to filter noise from the images, which was implemented using the wdenoise2() function in MATLAB 2021.

References:

[1] Milan, S., Vaclav, H., Roger, B., 2015. Image processing, analysis and machine vision. Cengage Learning: Stamford. [2] Kumar, P.L.K., Sharma, P., Pandey, C., et al., 2014. Geospatial strategy for tropical forest-wildlife reserve biomass estimation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 6(2), 917-923. [3] Borra, S., Thanki, R.M., Dey, N., 2019. Satellite image analysis: Clustering and classification. Singapore Springer: Singapore. [4] William, E.H., 2016. Technique for enhancing digital image [Master’s thesis]. Edinburgh: Heriot Watt University. [5] Nasir, M., Nazamabadi-Pour, H., 2009. Image denoising in the wavelet domain using a new adaptive thresholding function. Neurocomputing. 72(4-6), 1012-1025. [6] Zhang, X.P., 2001. Thresholding neural network for adaptive noise reduction. IEEE Transactions on Neural Networks. 12(3), 567-584. [7] Noorbarkhsh A.G., Hui, G., Said, P., et al., 2021. Satellite multispectral and hyperspectral image denoising with enhanced adaptive generalized Gaussian distribution threshold in the wavelet domain. Remote Sensing. 13(1), 101. [8] He, Q., Chen, C., 2014. A new approach for atmospheric correction of MODIS imagery in turbid coastal waters: A case study for the Pearl River Estuary. Remote Sensing Letter. 5(3), 249-257. [9] Golilarz, N.A., Mirmozaffari, M., Gashteroodkhani, T.A., Ali, L., Dolatsara, H.A., Boskabadi, A. and Yazdi, M., 2020. Optimized wavelet-based satellite image de-noising with multi-population differential evolution-assisted harris hawks optimization algorithm. Ieee Access, 8, pp.133076-133085. DOI: https://doi.org/10.1109/ACCESS.2020.3010127 [10] Mohammed, K., Ehlem, Z., Mohammed, D., 2017. Satellite image denoising via Adjustment of the quaternoinic wavelet coefficients. 5th International Conference on Electrical Engineering—Boumerdes (ICEE-B); 2017 Oct 29-31; Boumerdes. New York: IEEE. p. 1-6. [11] Nasri, M., Nezamabadi-pour, H., 2009. Image denoising in the wavelet domain using a new adaptive thresholding function. Neurocomputing. 72(4), 1012-1025. [12] Golilarz, N.A., Demirel, H., 2017. Image de-noising using un-decimated wavelet transform (UWT) with soft thresholding technique. 9th International Conference on Computational Intelligence and Communication Networks (CICN); 2017 Sep 16-17; Girne. New York: IEEE. p. 16-19. [13] Golilarz, N.A., Gao, H., Ali, W., et al., 2018. Hyper-spectral remote sensing image de-noising with three dimensional wavelet transform utilizing smooth nonlinear soft thresholding function. 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP); 2018 Dec 14-16; Chengdu. New York: IEEE. p. 142-146. [14] Olaniyan, A., 2018. Once upon a Game Reserve: Sambisa and the Tragedy of a Forest Landscape [Internet]. Environmental Society Portal Academia (Spring 2018) no. 2. Rachel Carson center for Environmental Society. DOI: https://doi.org/10.5282/rcc/8176