Revisiting the digital noise reduction in automatic contouring of “ice-water” objects

This work describes the practical implementation of the method for digital noise suppression during processing images containing ice information to recognize automatically the contours of «ice-water» objects during aerial photography. Images containing ice information have special characteristic structural features related to noise, e.g.

granularity, glare, ice crumbs. This makes difficult or even impossible to recognize automatically the contours of ice-water objects. It is known that the success of the application of edge recognition methods depends on how much image noise is reduced. The paper discusses the construction method for the management of noise. The method is based on the sequential application of the Haar wavelet transform denoising using thresholding, clustering by k-means method. For the subsequent automatic construction of ice floes contours the Sobel operator is applied.

The aim of the work is to develop a method capable to process digital images effectively that contain ice information with strong digital noise. In this work we treated the images of one-year ice containing strong digital image noise in the form of granularity and in the form of ice crumbs. A description of the features of each of the steps of the proposed method and practical application is given.

As a result, the method was developed for processing images of ice information containing digital noise in absolute value commensurate with the basic data. It was noted that the use of the k-means method expands the scope. The k-rare method allows more detailed processing of ice information and distinguishes not only the contours of ice-water objects, but also the contours of ice crumbs.

The conclusion formulates the main advantages of the method and the possible application of the algorithm in the process of local exploration of the ice conditions of the Northern Sea Route channel using unmanned aerial vehicle for aerial photography. The usage of unmanned aerial vehicle for aerial photography will increase the frequency of weather forecast updates and predict the appearance of ice objects at the ship’s heading. That will allow us to select the safest and most economical efficient route along the Northern Sea Route.

The authors have no competing interests.

1. Dobrodeev A.F., Klementyeva N.Y., Sazonov K.E. A skewed movement of large-size ships in “narrow” ice channel. Problemy Arktiki I Antarcktiki. Arctic and Antarctic Research. 2018, 64, 2: 200–207. [In Russian].

2. Mironov E.U., Smirnov V.G., Bychkova I.A., Klyachkin S.V., Dyment L.N., Zakhvatkina N.Yu., May R.I., Guzenko R.B., Sapershtejn E.B., Mihalceva S.V., Platonova E.V., Starcev L.A. Experimental hardware-software complex of satellite monitoring and forecast of ice conditions. Problemy Arktiki I Antarktiki. Arctic and Antarctic Research. 2017, 2 (112): 15–26. [In Russian].

3. Moscovskiy S.B., Sergeev A.N., Lalina N.A. Cleaning of a signal from noise by using Wavelet transformation. Universum: Technical sience, digital journal. 2015, № 2 (15). Available at: http://7universum.com/ru/tech/archive/item/1958 (accessed 24.03.2020).

4. Bui T.Ch., Spitsyn V.G. Analysis of methods of digital images edge detection. Doklady TUSUR. Proceedings of TUSUR. 2010, 2 (22): 221–223. [In Russian].

5. Marusina M.Ya., Anodina-Andrievskaya E.M. Wavelet analysis in tomographic imagesprocessing. Nauchnoe Priborostroenie. Scientific Instrumentation. 2011, 21: 71–75. [In Russian].

6. Zvyagin K.N., Voitkunskaia A.Ya., Maltcev D.D. Tertiary treatment of radar information using wavelet transformation. Morskie intellektualnye tehnologii. Marine Intellectual Technologies. 2018, 1 (4): 218–223. [In Russian].

7. Suchanov R.A., Khaidarov A.S. Application of Sobel filter for tectonic zoning on the example of Jurassic deposits for one of the fields of “Slavneft-Megionneftegaz”. PROneft’. Nauchno-tehnicheskiy zhurnal “Gazprom-Oil”. Journal of “Gazprom-Oil”. 2017, 1 (3): 20–22. [In Russian].

8. Obidin M.V., Serebroski A.P. Wavelets and adaptive thresholding. Informacionnye processy. Information processes. 2013, 13 (2): 91–99. [In Russian].

9. Evtikhiev N.N., Starikov S.N, Cheremkhin P.A. Estimating how the dynamic range and noise of the recording cameras affect the quality of digital holograms. Opticheskii zhurnal. Journal of Optical Technology. 2013, 80 (5): 301–308.

10. Likas A., Vlassis N., Verbeek J. The Global K-means clustering algorithm. Pattern Recognition. 2003, 36: 451–461.