Sharpness indicator of aerial- and space images obtained for mapping purposes

Aerophototopography (Aerial Surveying And Photogrammetry)
UDC: 
528.7
DOI: 
10.22389/0016-7126-2020-960-6-35-44
1 Anikeeva I.A.
Year: 
2020
№: 
6
960
Pages: 
35-44

Roscartography, JSC

1, 
Abstract:
The main characteristics of aerial- and space imagery, obtained for mapping purposes, are the distinct details transferring and sharpness, which determine their visual properties. The sharpness of aerial- and space images affects the accuracy of measurements made on them, as well as the quality products obtained. The ability of the image to transfer fine details is separately determined by its actual spatial resolution. It is shown that the actual resolution does not completely represent the structural-and-geometric characteristics of the images’ fine quality. Calculations showing that neither actual spatial resolution nor the gradation characteristic “local contrast” make it possible to evaluate its sharpness are presented. The author proposes a method of numerically image sharpness assessing, based on the gradient characteristic of the edge profile curve. The advantage of this method in comparison with alternative ones is shown. The general form of the image sharpness criteria calculating formula for any radiometric resolution is given. The proposed method enables obtaining a normed value, varying from 0 to 1, which provides obvious interpretability of the result. The permissible value of the sharpness index of aerial and space imagery obtained for mapping is determined. Examples of images obtained by an aerial survey complex based on a light drone, as well as those obtained by a remote sensing spacecraft, satisfying the established acceptable sharpness criteria, are given. The correspondence of obtained numerical estimates to the visual perception of the image sharpness is shown.
I would like to thank JSC Ural-Siberian Geoinformation Company, in particular it’s chief engineer Elena Kobzeva, for the aerial images provided for the experiment and assistance in the idea of evaluating the photographic sharpness of images
References: 
1.   Anikeeva I.A., Kadnichanskiy S.A. (2017) Evaluation of the actual resolution of digital aerial and satellite imagery using an edge profile curve. Geodezia i Kartografia, 78(6), pp. 25-36. (In Russian). DOI: 10.22389/0016-7126-2017-924-6-25-36.
2.   Fotokinotechnika: Enciklopedija. Glavnyj redactor E. A. Iofis. Moscow: Sovetskaja enciklopedija, 1981, 447 p.
3.   Kadnichanskiy S.A. (2018) Сontrast evaluation of digital aerial and satellite images. Geodezia i Kartografia, 79(3), pp. 46-51. (In Russian). DOI: 10.22389/0016-7126-2018-933-3-46-51.
4.   Kuchko A.S. Aerofotografiya. Osnovy i metrologiya [Aerial Photography. Fundamentals and Metrology]. M.: Nedra, 1974, 272 p.
5.   Sai S. V., Sorokin N. Yu. Metod otsenki chetkosti fotorealistichnykh izobrazhenii. Elektronika, izmeritel'naya tekhnika, radiotekhnika i svyaz'. Dokl. TUSURa, 2012, Vol. 6, no. 2, pp. 78–82.
6.   Gvozden G., Grgic S., Grgic M. (2018) Blind image sharpness assessment based on local contrast map statistics. Journal of Visual Communication and Image Representation, no. 50, pp. 145-148.
7.   Chen M. J., Bovik A. C. (2014) No-reference image blur assessment using multiscale gradient. EURASIP Journal on Image and Video Processing, no. 1, pp. 1-11. DOI: 10.1186/1687-5281-2014-40.
8.   Ciancio A., Da Costa A. T., Da Silva A. B., Said A., Sama- dani R., Obrador P. (2011) No-reference blur assessment of digital pictures based on multifeatureclassifiers. IEEE Transactions on Image Processing, no. 1, pp. 64-75. DOI: 10.1109/TIP.2010.2053549.
9.   Cohen E., Yitzhaky Y. (2010) No-reference assessment of blur and noise impacts on image quality. Signal, Image and Video Processing, no. 4, pp. 289-302. DOI: 10.1007/s11760-009-0117-4.
10.   Feichtenhofer C., Fassold H., Schallauer P. (2013) A Perceptual Image Sharpness Metric Based on Local Edge Gradient Analysis. IEEE Signal Processing Letters, Volume 20, no. 4, pp. 379-382. DOI: 10.1109/LSP.2013.2248711.
11.   Ferzli R., Karam L. J. (2009) A no-reference objective image sharpness metric based onthe notion of just noticeable blur (JNB). IEEE Transactions on Image Processing, Volume 18, no. 4, pp. 717-728. DOI: 10.1109/TIP.2008.2011760.
12.   Jiao S., Qi H., Lin W., Shen W. (2013) Fast and efficient blind image quality index inspatial domain. Electronics Letters, Volume 49, no. 18, pp. 1137-1138. DOI: 10.1049/el.2013.1837.
13.   Kamble V., Bhurchandi K. M. (2015) No-reference image quality assessment algorithms: A survey. Optik, no. 126, pp. 11-12. DOI: 10.1016/j.ijleo.2015.02.093.
14.   Li C., Bovik A.C., Wu X. (2011) Blind image quality assessment using a general regression neural network. IEEE Transactions on Neural Networks, Volume 22, no. 5, pp. 793-799. DOI: 10.1109/TNN.2011.2120620.
15.   Liang L., Wang S., Chen J., Ma S., Zhao D., Gao W. (2010) No-reference perceptual image quality metric using gradient profiles for JPEG2000. Signal Processing: Image Communication, Volume 25, no. 7, pp. 502-516.
16.   Marais I. V. Z., Steyn W. H. (2007) Robust defocus blur identification in the context of blind image quality assessment. Signal Processing: Image Communication, Volume 22, no. 10, pp. 833-844.
17.   Moorthy A. K., Bovik A. C. (2011) Blind image quality assessment: from naturalscene statistics to perceptual quality, IEEE Trans. Image Process. IEEE Transactions on Image Processing, Volume 20, no. 12, pp. 3350-3364. DOI: 10.1109/TIP.2011.2147325.
18.   Narvekar N. D., Karam L. J. (2011) A no-reference image blur metric based on the cumu-lative probability of blur detection (CPBD). IEEE Transactions on Image Processing, Volume 20, no. 9, pp. 2678-2683. DOI: 10.1109/TIP.2011.2131660.
19.   Qian J., Zhao H., Fu J., He G., Hou X., Fang B., Qian J. (2018) No-reference image sharpness metric based on directional derivatives. International Conference on Security, Pattern Analysis, and Cybernetics (SPAC). pp. 340-344. DOI: 10.1109/SPAC.2017.8304301.
20.   Sheikh H.R., Sabir M.F. and Bovik A.C. (2006) A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on Image Processing, Volume 15, no. 11, pp. 3440-3451.
21.   Sheikh H. R., Wang Z., Cormack L., Bovik A. C. LIVE Image Quality Assessment Database Release 2. URL: live.ece.utexas.edu/research/quality (accessed: 13.01.2020).
22.   Shen J., Li Q., Erlebacher G. (2011) Hybrid no-reference natural image quality assessment of noisy, blurry, JPEG2000, and JPEG images. IEEE Transactions on Image Processing, Volume 20, no. 8, pp. 2089-2098. DOI: 10.1109/TIP.2011.2108661.
23.   Wang Z., Bovik A.C., Sheikh H. R., Simoncelli E. P. (2004) Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, Volume 13, no. 4, pp. 600-612. DOI: 10.1109/TIP.2003.819861.
24.   Wu S., Lin W., Xie S., Lu Z., Ong E. P., Yao S. (2009) Blind blur assessment for vision-basedapplications. Journal of Visual Communication and Image Representation, Volume 20, no. 4, pp. 231-241.
25.   Laboratory for Image and Video Engineering. URL: https://www.live.ece.utexas.edu/research/quality/subjective.htm (accessed: 13.01.2020).
Citation:
Anikeeva I.A., 
(2020) Sharpness indicator of aerial- and space images obtained for mapping purposes. Geodesy and cartography = Geodezia i Kartografia, 81(6), pp. 35-44. (In Russian). DOI: 10.22389/0016-7126-2020-960-6-35-44
Publication History
Received: 10.12.2019
Accepted: 01.06.2020
Published: 20.07.2020

Content

2020 June DOI:
10.22389/0016-7126-2020-960-6