DOI: https://doi.org/10.20535/kpi-sn.2020.2.197955

MULTISPECTRAL IMAGE COMBINING AS A METHOD OF INFORMATION CONTENT INCREASING AT BINARY SEGMENTATION

Denys V. Storozhyk, Oleksandr V. Muraviov, Anatoliy G. Protasov, Viktor G. Bazhenov, Galina A. Bohdan

Abstract


Background. One of the biggest tech trends in manufacture and equipment engineering today is the desire to automate processes in order to increase their efficiency and minimize the influence of the human factor. A promising direction for the implementation of this concept is computer vision methods based on the automation of image processing and analysis. Machine learning allows systems, such as artificial neural networks, to detect, recognize and classify objects. All this has become possible due to the development of image processing methods, which include binary segmentation. The information content increase of the result with this data processing method is possible using the multispectral complexing algorithm, which will improve the efficiency of machine vision systems and automated image processing.

Objective. The purpose of the paper is the investigation of applicability of combining multispectral images methods to increase the information content and quality of the result of data processing based on binary segmentation.

Methods. For investigating of the combining images methods the software environment implemented on the C# programming language basis was used with mathematical apparatus of binary segmentation. The results analysis was made using methods of statistical data processing.

Results. Effectiveness estimates of the following multispectral image combining methods applying to improve the quality of binary segmentation: averaging, maximum, interlaced combining and interlaced combining of maxima are obtained. The research was made both for images obtained at favorable conditions and in the presence of various types of noises, which decrease the information content of images in different spectral ranges.

Conclusions. Using multispectral image combining methods allows increasing the information content of the binary segmentation result up to 15%. Particularly significant quality improvement is manifested in presence of noises on various spectral range images, such as flare or low light.

Keywords


Binary segmentation; Image processing; Image combining methods; Multispectral images

References


G. Tolias et al., “Image search with selective match kernels: aggregation across single and multiple images”, Int. J. Comput. Vis., vol. 116, pp. 247–261, 2016. doi: 10.1007/s11263-015-0810-4

O.B. Zhukov et al., “Artificial intelligence in medicine: from hybrid studies and clinical validation to the development of application models”, Andrologija i Genital'naja Hirurgija, vol. 20, no. 3, pp. 15–19, 2019. doi: 10.17650/2070-9781-2019-20-3-15-19

L. Galli and D. de Candia, “Multispectral image segmentation via multiscale weighted aggregation method”, in Proc. SPIE 5982, Image and Signal Processing for Remote Sensing XI, 2005. doi: 10.1117/12.627534

Y. Yuan et al., “Specific light-up bioprobe with aggregation‐induced emission and activatable photoactivity for the targeted and image‐guided photodynamic ablation of cancer cells”, Angew. Chem. Int. Ed., vol. 54, pp. 1780–1786, 2015. doi: 10.1002/anie.201408476

A. Protasov, “Reconstruction of the thermal field image from measurements in separate points”, in Proc. IEEE Int. Conf. Microwaves, Radar and Remote Sensing Symposium (MRRS), 2017, pp. 89–92. doi: 10.1109/MRRS.2017.8075035

K. Rokni et al., “A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques”, Int. J. of Appl. Earth Observ. Geoinform., vol. 34, pp. 226–234, 2015. doi: 10.1016/j.jag.2014.08.014

D. Paternain et al., “Construction of image reduction operators using averaging aggregation functions”, Fuzzy Sets and Systems, vol. 261, pp. 87–111, 2015. doi: 10.1016/j.fss.2014.03.008

E. Ricchetti, “Multispectral satellite image and ancillary data integration for geological classification”, Photogrammetric Engineering and Remote Sensing, vol. 66, pp. 429–435, 2000.

A.S. Vasilev et al., “Quality assessment criteria for image fusion in multispectral optical-electronic systems”, J. Instrument Eng., vol. 60, no. 7, pp. 647–653, 2017. doi: 10.17586/0021-3454-2017-60-7-647-653

A.V. Zakharov et al., “Criteria for assessing the quality of image segmentation”, Trudy NIISI RAN, vol. 2, no. 2, pp. 87–99, 2012.

M.S. Mamuta, “Effectiveness of the complex of optical and electronic systems”, PhD dissertation, NTUU KPI, Kyiv, 2013.

G. Hong et al., “A wavelet and IHS integration method to fuse high resolution SAR with moderate resolution multispectral images”, Photogrammetric Engineering and Remote Sensing, vol. 75, pp. 1213–1223, 2009. doi: 10.14358/PERS.75.10.1213

C. Zhu et al., “Multi-image aggregation for better visual object retrieval”, in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), Florence, 2014, pp. 4304–4308. doi: 10.1109/ICASSP.2014.6854414

A.V. Muravyov and E.A. Nazarchuk, “Thermal stabilization of image quality of an optical thermograph system”, Visnyk Inzhenernoi Akademii Ukrainy, no. 4, pp. 195–199, 2016.

A. Momot and R. Galagan, “Influence of architecture and training dataset parameters on the neural networks efficiency in thermal nondestructive testing”, Sciences of Europe, vol. 1, no. 44, pp. 20–25, 2019.


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