KaydedilenlerDerginizi ekleyin

Dergiler / Mersin Photogrammetry Journal / 2023 / Cilt: 5 Sayı: 1

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

Mehmet Akif GÜNEN
Erkan BEŞDOK
Sayfa
1–9
Yayın Tarihi
2023-06-30
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Hyperspectral images are widely used for land use/cover analysis in remote sensing due to their rich spectral information.
📊
Temel Bulgular & Veri: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.
💡
Sonuç & Katkı: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.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Denoising · remote sensing · hyperspectral

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.

🔖

Bu Makaleye Atıf Yap

GÜNEN, M. A., BEŞDOK, E. (2023). Effect of denoising methods for hyperspectral images classification: DnCNN, NGM, CSF, BM3D and Wiener. Mersin Photogrammetry Journal, Cilt: 5 Sayı: 1, 1–9. https://ajindex.com/articles/952682

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (10)

  1. 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.
  2. 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.
  3. 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.
  4. 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).
  5. 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).
  6. 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.
  7. 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).
  8. 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).
  9. 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.
  10. 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