COMBINED VISUAL QUALITY METRIC OF REMOTE SENSING IMAGES BASED ON NEURAL NETWORK
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Schowengerdt, R. A. Remote sensing, models, and methods for image processing, 3rd ed. Academic Press, Burlington Publ., MA, 2007. 560 p.
Khorram, S., van der Wiele, C. F., Koch, F. H., Nelson, S. A. C., Potts, M. D. Future Trends in Remote Sensing. Principles of Applied Remote Sensing, Springer International Publishing: Cham, 2016, pp. 277–285.
Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., Jepsen, M., Kuemmerle, T., Meyfroidt, P., Mitchard, E. A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring. Remote Sensіng, 2016, vol. 8, no. 1, article id: 70. 23 p. DOI: 10.3390/rs8010070.
Deledalle, C.-A., Denis, L., Tabti, S., Tupin, F. MuLoG, or How to Apply Gaussian Denoisers to Multi-Channel SAR Speckle Reduction? IEEE Transactions on Image Processing, 2017, vol. 26, pp. 4389–4403. DOI: 10.1109/TIP.2017.2713946.
Zhong, P., Wang, R. Multiple-Spectral-Band CRFs for Denoising Junk Bands of Hyperspectral Imagery. IEEE Transactions on Geoscience and Remote Sensing, 2013, vol. 51, pp. 2260–2275. DOI: 10.1109/TGRS.2012.2209656.
van Zyl Marais, I., Steyn, W. H., du Preez, J. A. Onboard image quality assessment for a small low earth orbit satellite. Proceedings of the 7th IAA Symposium on Small Satellites for Earth Observation, 2009. 7 p.
Shi, X., Wang, L., Shao, X., Wang, H., Tao, Z. Accurate estimation of motion blur parameters in noisy remote sensing image. Proceedings of the SPIE Sensing Technology + Applications, Baltimore, 2015. 10 p. DOI: 10.1117/12.2176893.
Abramov, S., Uss, M., Lukin, V., Vozel, B., Chehdi, K., Egiazarian, K. Enhancement of Component Images of Multispectral Data by Denoising with Reference. Remote Sensing, 2019, vol. 11, iss. 6, article id: 611. 16 p. DOI: 10.3390/rs11060611.
Dellepiane, S. G., Angiati, E. Quality Assessment of Despeckled SAR Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, vol. 7, pp. 691–707. DOI: 10.1109/JSTARS.2013.2279501.
Lin, W., Jay Kuo, C. C. Perceptual visual quality metrics: A survey. Journal of Visual Communication and Image Representation, 2011, vol. 22, no. 4, pp. 297-312. DOI: 10.1016/j.jvcir.2011.01.005.
Lukin, V., Abramov, S., Krivenko, S., Kurekin, A., Pogrebnyak, O. Analysis of classification accuracy for pre-filtered multichannel remote sensing data. Expert Systems with Applications, 2013, vol. 40, pp. 6400–6411. DOI: 10.1016/j.eswa.2013.05.061.
Aswathy, C., Sowmya, V., Soman, K.P. Hyperspectral Image Denoising Using Low Pass Sparse Banded Filter Matrix for Improved Sparsity Based Classification. Procedia Comput. Sci. 2015, vol. 58, pp. 26–33. DOI: 10.1016/j.procs.2015.08.005.
Yang, K., Jiang, H. Optimized-SSIM Based Quantization in Optical Remote Sensing Image Compression. Proceedings of the 2011 Sixth International Conference on Image and Graphics, Hefei, Anhui, China, 2011, pp. 117–122, DOI: 10.1109/ICIG.2011.38.
Yuan, T., Zheng, X., Hu, X., Zhou, W., Wang, W. A Method for the Evaluation of Image Quality According to the Recognition Effectiveness of Objects in the Optical Remote Sensing Image Using Machine Learning Algorithm. PLoS ONE, 2014, vol. 9(1). 8 p. DOI: 10.1371/journal.pone.0086528.
Guo, J., Yang, F., Tan, H., Wang, J., Liu, Z. Image matching using structural similarity and geometric constraint approaches on remote sensing images. J. Appl. Remote Sens., 2016, vol. 10, iss. 4, article id: 045007. 12 p. DOI: 10.1117/1.JRS.10.045007.
Chandler, D. M. Seven Challenges in Image Quality Assessment: Past, Present, and Future Research. Signal Process., 2013, pp. 1–53. DOI: 10.1155/2013/905685.
Ponomarenko, N., Jin, L., Ieremeiev, O., Lukin, V., Egiazarian, K., Astola, J., Vozel, B., Chehdi, K., Carli, M., Battisti, F. Image database TID2013: Peculiarities, results and perspectives. Signal Processing: Image Communications, 2015, vol. 30, pp. 57–77. DOI: 10.1016/j.image.2014.10.009.
Lin, H., Hosu, V., Saupe, D. KADID-10k: A Large-scale Artificially Distorted IQA Database. Proceedings of the 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), Berlin, 2019, pp. 1–3. DOI: 10.1109/QoMEX.2019.8743252.
Sun, W., Zhou, F., Liao, Q. MDID: A multiply distorted image database for image quality assessment. Pattern Recognitition, 2017, vol. 61, pp. 153–168. DOI: 10.1016/j.patcog.2016.07.033.
Ieremeiev, O., Lukin, V., Ponomarenko, N., Egiazarian, K. Robust linearized combined metrics of image visual quality. Electronic Imaging, Image Processing: Algorithms and Systems XVI, 2018, pp. 260-1-260–6. DOI: 10.2352/ISSN.2470-1173.2018.13.IPAS-260.
Okarma, K. Combined Full-Reference Image Quality Metric Linearly Correlated with Subjective Assessment. In Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, 2010, vol. 6113, pp. 539–546. DOI: 10.1007/978-3-642-13208-7_67.
Lukin, V. V., Ponomarenko, N. N., Ieremeiev, O. I., Egiazarian, K. O., Astola, J. Combining full-reference image visual quality metrics by neural network. SPIE/IS&T Electronic Imaging, 2015, vol. 9394. 6 p. DOI: 10.1117/12.2085465.
Bosse, S., Maniry, D., Muller, K.-R., Wiegand, T., Samek, W. Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment. IEEE Transactions on Image Processing, 2018, vol. 27, pp. 206–219. DOI: 10.1109/TIP.2017.2760518.
Lukin, V., Zemliachenko, A., Krivenko, S., Vozel, B., Chehdi, K. Lossy Compression of Remote Sensing Images with Controllable Distortions. Satellite Information Classification and Interpretation, IntechOpen, 2019. 17 p.
Ieremeiev, O. List of the full-reference metrics. Available at: https://github.com/OlegIeremeiev/IQA/wiki/MRRS2020. (accessed 5.10.2020).
Bosse, S., Maniry, D., Muller, K.-R., Wiegand, T., Samek, W. Neural network-based full-reference image quality assessment. Proceedings of the 2016 Picture Coding Symposium (PCS), Nuremberg, 2016, pp. 1–5. DOI: 10.1109/PCS.2016.7906376.
Athar, S., Wang, Z. A Comprehensive Performance Evaluation of Image Quality Assessment Algorithms. IEEE Transactions on Image Processing, 2019, vol. 7, pp. 140030–140070. DOI: 10.1109/ACCESS.2019.2943319.
Tibshirani, R. Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological), 1996, vol. 58, pp. 267–288. DOI: 10.1111/j.2517-6161.1996.tb02080.x.
DOI: https://doi.org/10.32620/reks.2020.4.01
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