Research Article
BibTex RIS Cite
Year 2023, Volume: 5 Issue: 1, 1 - 9, 30.06.2023
https://doi.org/10.53093/mephoj.1213166

Abstract

References

  • Günen, M. A., Atasever, U. H., & Beşdok, E. (2020). Analyzing the contribution of training algorithms on deep neural networks for hyperspectral image classification. Photogrammetric Engineering & Remote Sensing, 86(9), 581-588.
  • Nofrizal, A. Y., Sonobe, R., Hiroto, Y., Morita, A., & Ikka, T. (2022). Estimating chlorophyll content of Zizania latifolia with hyperspectral data and random forest. International Journal of Engineering and Geosciences, 7(3), 221-228.
  • Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017). Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE transactions on image processing, 26(7), 3142-3155.
  • Lam, A., Sato, I., & Sato, Y. (2012, November). Denoising hyperspectral images using spectral domain statistics. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012) (pp. 477-480). IEEE.
  • Annam, S., & Singla, A. (2020, November). Correlative analysis of denoising methods in spectral images embedded with different noises. In 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) (pp. 318-323). IEEE.
  • Momeny, M., Jahanbakhshi, A., Neshat, A. A., Hadipour-Rokni, R., Zhang, Y. D., & Ampatzidis, Y. (2022). Detection of citrus black spot disease and ripeness level in orange fruit using learning-to-augment incorporated deep networks. Ecological Informatics, 71, 101829.
  • Yuksel, M. E., & Besdok, E. (2004). A simple neuro-fuzzy impulse detector for efficient blur reduction of impulse noise removal operators for digital images. IEEE Transactions on Fuzzy Systems, 12(6), 854-865.
  • Buades, A., Coll, B., & Morel, J. M. (2005, June). A non-local algorithm for image denoising. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05) (Vol. 2, pp. 60-65). IEEE.
  • Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2006, February). Image denoising with block-matching and 3D filtering. In Image processing: algorithms and systems, neural networks, and machine learning (Vol. 6064, pp. 354-365). SPIE.
  • Aharon, M., Elad, M., & Bruckstein, A. (2006). K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on signal processing, 54(11), 4311-4322.
  • Li, S. Z. (2009). Markov random field modeling in image analysis. Springer Science & Business Media.
  • He, W., Yao, Q., Li, C., Yokoya, N., Zhao, Q., Zhang, H., & Zhang, L. (2020). Non-local meets global: An iterative paradigm for hyperspectral image restoration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4), 2089-2107.
  • Gu, S., Zhang, L., Zuo, W., & Feng, X. (2014). Weighted nuclear norm minimization with application to image denoising. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2862-2869).
  • Ghael, S., Sayeed, A. M., & Baraniuk, R. G. (1997, July). Improved wavelet denoising via empirical Wiener filtering. In SPIE Technical Conference on Wavelet Applications in Signal Processing.
  • Schmidt, U., & Roth, S. (2014). Shrinkage fields for effective image restoration. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2774-2781).
  • Günen, M. A., Atasever, U. H., & Beşdok, E. (2020). Analyzing the contribution of training algorithms on deep neural networks for hyperspectral image classification. Photogrammetric Engineering & Remote Sensing, 86(9), 581-588.
  • Yüksel, M. E., Baştürk, A., & Beşdok, E. (2004). Detail-preserving restoration of impulse noise corrupted images by a switching median filter guided by a simple neuro-fuzzy network. EURASIP Journal on Advances in Signal Processing, 2004(16), 1-11.
  • Çivicioğlu, P., Alçı, M., & Beşdok, E. (2004). Impulsive noise suppression from images with the noise exclusive filter. EURASIP Journal on Advances in Signal Processing, 1-7.
  • Beşdok, E. (2004). A new method for impulsive noise suppression from highly distorted images by using Anfis. Engineering Applications of Artificial Intelligence, 17(5), 519-527.
  • Kumar, N., & Nachamai, M. (2017). Noise removal and filtering techniques used in medical images. Oriental Journal of Computer Science & Technology, 10(1), 103-113.
  • Patidar, P., Gupta, M., Srivastava, S., & Nagawat, A. K. (2010). Image de-noising by various filters for different noise. International Journal of Computer Applications, 9(4), 45-50.
  • Kanagalakshmi, K., & Chandra, E. (2011, April). Performance evaluation of filters in noise removal of fingerprint image. In 2011 3rd International Conference on Electronics Computer Technology (Vol. 1, pp. 117-121). IEEE.
  • Niknejad, M., & Figueiredo, M. A. (2018, September). Poisson image denoising using best linear prediction: a post-processing framework. In 2018 26th European Signal Processing Conference (EUSIPCO) (pp. 2230-2234). IEEE.
  • Atasever, U. H., & Gunen, M. A. (2021). Change detection approach for SAR imagery based on arc-tangential difference image and k-Means++. IEEE Geoscience and Remote Sensing Letters, 19, 1-5.
  • He, W., Yao, Q., Li, C., Yokoya, N., & Zhao, Q. (2019). Non-local meets global: An integrated paradigm for hyperspectral denoising. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6868-6877).
  • Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2007). Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on image processing, 16(8), 2080-2095.
  • Juang, L. H., & Wu, M. N. (2010). Image noise reduction using Wiener filtering with pseudo-inverse. Measurement, 43(10), 1649-1655.
  • Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1), 21-27.
  • Günen, M. A. (2022). Performance comparison of deep learning and machine learning methods in determining wetland water areas using EuroSAT dataset. Environmental Science and Pollution Research, 29(14), 21092-21106.
  • Singh, S., Singh, D., Sajwan, M., Rathor, V. S., & Garg, D. (2022). Hyperspectral image classification using multiobjective optimization. Multimedia Tools and Applications, 81(18), 25345-25362.
  • Park, D. Y., & Park, J. H. (2020). Hologram conversion for speckle free reconstruction using light field extraction and deep learning. Optics Express, 28(4), 5393-5409.
  • Karthik, R., Hariharan, M., Anand, S., Mathikshara, P., Johnson, A., & Menaka, R. (2020). Attention embedded residual CNN for disease detection in tomato leaves. Applied Soft Computing, 86, 105933.

Effect of denoising methods for hyperspectral images classification: DnCNN, NGM, CSF, BM3D and Wiener

Year 2023, Volume: 5 Issue: 1, 1 - 9, 30.06.2023
https://doi.org/10.53093/mephoj.1213166

Abstract

Hyperspectral images are widely used for land use/cover analysis in remote sensing due to their rich spectral information. However, these data often suffer from noise caused by various factors such as random and systematic errors, making them less useful for end-users. In this study, denoising methods (i.e., DnCNN, NGM, CSF, BM3D, and Wiener) for hyperspectral images were compared using the Pavia University hyperspectral dataset with four different noise types: Gaussian, Salt & Pepper, Poisson, and Speckle. After denoising, the k-nearest neighbor method was used to classify the image, and statistical and visual performance comparisons were performed on the classified data. Six performance metrics -Accuracy, Sensitivity, Specificity, Precision, F-Score, and G-Mean- were employed to compare the outcomes qualitatively. The findings demonstrate that DnCNN and BM3D have the best outcome performance for all four noise types. Due to their lack of sensitivity and specificity, the CSF and Wiener approaches had low performance for particular noise sources. For all noise types, the NGM approach had the worst results. The validated instruments not provide effective results when it came to denoising Salt & Pepper noise, but they managed to produce outstanding results when it came to denoising Poisson noise. In order to enhance the quality and usability of hyperspectral images for land use/cover analysis, this study emphasizes the significance of choosing an effective denoising technique.

References

  • Günen, M. A., Atasever, U. H., & Beşdok, E. (2020). Analyzing the contribution of training algorithms on deep neural networks for hyperspectral image classification. Photogrammetric Engineering & Remote Sensing, 86(9), 581-588.
  • Nofrizal, A. Y., Sonobe, R., Hiroto, Y., Morita, A., & Ikka, T. (2022). Estimating chlorophyll content of Zizania latifolia with hyperspectral data and random forest. International Journal of Engineering and Geosciences, 7(3), 221-228.
  • Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017). Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE transactions on image processing, 26(7), 3142-3155.
  • Lam, A., Sato, I., & Sato, Y. (2012, November). Denoising hyperspectral images using spectral domain statistics. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012) (pp. 477-480). IEEE.
  • Annam, S., & Singla, A. (2020, November). Correlative analysis of denoising methods in spectral images embedded with different noises. In 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) (pp. 318-323). IEEE.
  • Momeny, M., Jahanbakhshi, A., Neshat, A. A., Hadipour-Rokni, R., Zhang, Y. D., & Ampatzidis, Y. (2022). Detection of citrus black spot disease and ripeness level in orange fruit using learning-to-augment incorporated deep networks. Ecological Informatics, 71, 101829.
  • Yuksel, M. E., & Besdok, E. (2004). A simple neuro-fuzzy impulse detector for efficient blur reduction of impulse noise removal operators for digital images. IEEE Transactions on Fuzzy Systems, 12(6), 854-865.
  • Buades, A., Coll, B., & Morel, J. M. (2005, June). A non-local algorithm for image denoising. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05) (Vol. 2, pp. 60-65). IEEE.
  • Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2006, February). Image denoising with block-matching and 3D filtering. In Image processing: algorithms and systems, neural networks, and machine learning (Vol. 6064, pp. 354-365). SPIE.
  • Aharon, M., Elad, M., & Bruckstein, A. (2006). K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on signal processing, 54(11), 4311-4322.
  • Li, S. Z. (2009). Markov random field modeling in image analysis. Springer Science & Business Media.
  • He, W., Yao, Q., Li, C., Yokoya, N., Zhao, Q., Zhang, H., & Zhang, L. (2020). Non-local meets global: An iterative paradigm for hyperspectral image restoration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4), 2089-2107.
  • Gu, S., Zhang, L., Zuo, W., & Feng, X. (2014). Weighted nuclear norm minimization with application to image denoising. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2862-2869).
  • Ghael, S., Sayeed, A. M., & Baraniuk, R. G. (1997, July). Improved wavelet denoising via empirical Wiener filtering. In SPIE Technical Conference on Wavelet Applications in Signal Processing.
  • Schmidt, U., & Roth, S. (2014). Shrinkage fields for effective image restoration. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2774-2781).
  • Günen, M. A., Atasever, U. H., & Beşdok, E. (2020). Analyzing the contribution of training algorithms on deep neural networks for hyperspectral image classification. Photogrammetric Engineering & Remote Sensing, 86(9), 581-588.
  • Yüksel, M. E., Baştürk, A., & Beşdok, E. (2004). Detail-preserving restoration of impulse noise corrupted images by a switching median filter guided by a simple neuro-fuzzy network. EURASIP Journal on Advances in Signal Processing, 2004(16), 1-11.
  • Çivicioğlu, P., Alçı, M., & Beşdok, E. (2004). Impulsive noise suppression from images with the noise exclusive filter. EURASIP Journal on Advances in Signal Processing, 1-7.
  • Beşdok, E. (2004). A new method for impulsive noise suppression from highly distorted images by using Anfis. Engineering Applications of Artificial Intelligence, 17(5), 519-527.
  • Kumar, N., & Nachamai, M. (2017). Noise removal and filtering techniques used in medical images. Oriental Journal of Computer Science & Technology, 10(1), 103-113.
  • Patidar, P., Gupta, M., Srivastava, S., & Nagawat, A. K. (2010). Image de-noising by various filters for different noise. International Journal of Computer Applications, 9(4), 45-50.
  • Kanagalakshmi, K., & Chandra, E. (2011, April). Performance evaluation of filters in noise removal of fingerprint image. In 2011 3rd International Conference on Electronics Computer Technology (Vol. 1, pp. 117-121). IEEE.
  • Niknejad, M., & Figueiredo, M. A. (2018, September). Poisson image denoising using best linear prediction: a post-processing framework. In 2018 26th European Signal Processing Conference (EUSIPCO) (pp. 2230-2234). IEEE.
  • Atasever, U. H., & Gunen, M. A. (2021). Change detection approach for SAR imagery based on arc-tangential difference image and k-Means++. IEEE Geoscience and Remote Sensing Letters, 19, 1-5.
  • He, W., Yao, Q., Li, C., Yokoya, N., & Zhao, Q. (2019). Non-local meets global: An integrated paradigm for hyperspectral denoising. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6868-6877).
  • Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2007). Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on image processing, 16(8), 2080-2095.
  • Juang, L. H., & Wu, M. N. (2010). Image noise reduction using Wiener filtering with pseudo-inverse. Measurement, 43(10), 1649-1655.
  • Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1), 21-27.
  • Günen, M. A. (2022). Performance comparison of deep learning and machine learning methods in determining wetland water areas using EuroSAT dataset. Environmental Science and Pollution Research, 29(14), 21092-21106.
  • Singh, S., Singh, D., Sajwan, M., Rathor, V. S., & Garg, D. (2022). Hyperspectral image classification using multiobjective optimization. Multimedia Tools and Applications, 81(18), 25345-25362.
  • Park, D. Y., & Park, J. H. (2020). Hologram conversion for speckle free reconstruction using light field extraction and deep learning. Optics Express, 28(4), 5393-5409.
  • Karthik, R., Hariharan, M., Anand, S., Mathikshara, P., Johnson, A., & Menaka, R. (2020). Attention embedded residual CNN for disease detection in tomato leaves. Applied Soft Computing, 86, 105933.
There are 32 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Mehmet Akif Günen 0000-0001-5164-375X

Erkan Beşdok 0000-0001-9309-375X

Early Pub Date May 27, 2023
Publication Date June 30, 2023
Published in Issue Year 2023 Volume: 5 Issue: 1

Cite

APA Günen, M. A., & Beşdok, E. (2023). Effect of denoising methods for hyperspectral images classification: DnCNN, NGM, CSF, BM3D and Wiener. Mersin Photogrammetry Journal, 5(1), 1-9. https://doi.org/10.53093/mephoj.1213166
AMA Günen MA, Beşdok E. Effect of denoising methods for hyperspectral images classification: DnCNN, NGM, CSF, BM3D and Wiener. MEPHOJ. June 2023;5(1):1-9. doi:10.53093/mephoj.1213166
Chicago Günen, Mehmet Akif, and Erkan Beşdok. “Effect of Denoising Methods for Hyperspectral Images Classification: DnCNN, NGM, CSF, BM3D and Wiener”. Mersin Photogrammetry Journal 5, no. 1 (June 2023): 1-9. https://doi.org/10.53093/mephoj.1213166.
EndNote Günen MA, Beşdok E (June 1, 2023) Effect of denoising methods for hyperspectral images classification: DnCNN, NGM, CSF, BM3D and Wiener. Mersin Photogrammetry Journal 5 1 1–9.
IEEE M. A. Günen and E. Beşdok, “Effect of denoising methods for hyperspectral images classification: DnCNN, NGM, CSF, BM3D and Wiener”, MEPHOJ, vol. 5, no. 1, pp. 1–9, 2023, doi: 10.53093/mephoj.1213166.
ISNAD Günen, Mehmet Akif - Beşdok, Erkan. “Effect of Denoising Methods for Hyperspectral Images Classification: DnCNN, NGM, CSF, BM3D and Wiener”. Mersin Photogrammetry Journal 5/1 (June 2023), 1-9. https://doi.org/10.53093/mephoj.1213166.
JAMA Günen MA, Beşdok E. Effect of denoising methods for hyperspectral images classification: DnCNN, NGM, CSF, BM3D and Wiener. MEPHOJ. 2023;5:1–9.
MLA Günen, Mehmet Akif and Erkan Beşdok. “Effect of Denoising Methods for Hyperspectral Images Classification: DnCNN, NGM, CSF, BM3D and Wiener”. Mersin Photogrammetry Journal, vol. 5, no. 1, 2023, pp. 1-9, doi:10.53093/mephoj.1213166.
Vancouver Günen MA, Beşdok E. Effect of denoising methods for hyperspectral images classification: DnCNN, NGM, CSF, BM3D and Wiener. MEPHOJ. 2023;5(1):1-9.