Application of Image Processing in Ice–Structure Interaction
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The understanding of Arctic physical processes and sustainable exploration, exploitation, and management of Arctic resources require more detailed, precise, and continuous measurements of sea ice parameters. Because various types of ice are in the ice-covered regions and the sizes of the ice floes can range from about 1 meter to kilometers, the temporally and spatially continuous field observations of sea ice are necessary for marine activities. One of the best ways of observing the ice conditions in the oceans is by using aerial or nautical imagery. The use of cameras as sensors on mobile sensor platforms (e.g., unmanned vehicles) will aid the development of sea ice observation. It has the potential of continuous measurements with high precision, which is particularly important for providing detailed localized information of sea ice to ensure safe operations of structures in ice-covered regions (Haugen et al. 2011).
The collected ice images or videos must be analyzed by dedicated computer algorithms to extract useful ice information to dynamic ice estimators and for decision support in Arctic offshore engineering. Therefore, this article introduces image processing algorithms for providing the ice parameters that are important factors in the analysis of ice–structure interaction in an ice field. These useful ice parameters include ice concentration, ice types, ice floe size, and floe size distribution, and they are defined as follows.
Floe is any relatively flat piece of sea ice 20 m or more across. It is subdivided according to horizontal extent. A giant flow is over 10 km across; a vast floe is 2–10 km across; a big floe is 500–2000 m across; a medium floe is 100–500 m across; and a small floe is 20–100 m across.
Ice cake is any relatively flat piece of sea ice less than 20 m across.
Brash ice is accumulations of floating ice made up of fragments not more than 2 m across and the wreckage of other forms of ice. It is common between colliding floes or in regions where pressure ridges have collapsed.
Slush is snow which is saturated and mixed with water on land or ice surfaces or as a viscous floating mass in water after heavy snowfall.
For simplicity, the size of sea ice piece is the only criterion to distinguish ice floe and brash ice in this research. That is, any relatively flat piece of sea ice 2 m or more across is considered as “ice floe,” while any relatively flat piece of sea ice less than 2 m across is considered as “brash ice (piece).” And the rest of ice pixels, e.g., single ice pixels or the ice pieces that are too small to be treated as brash ice, are considered as “slush.”
Ice Floe Size and Floe Size Distribution
The estimation of ice floe size and floe size distribution among the “ice floes” gives an important set of parameters from ice images. In image processing, the ice floe size can be determined by the number of pixels in the identified floe. If the focal length f and camera height are available, the actual size in SI unit of the ice floes and floe size distribution can also be calculated (Lu and Li 2010) by converting the image pixel size to its SI unit size.
Image Processing Methods
A digital image is a numeric representation of a two-dimensional picture, and it is composed of pixels which are the smallest individual elements in the image. A pixel holds quantized values that represent the color or gray level of the image at a particular point.
Ice Pixel Detection
Ice concentration, as defined, is a binary decision of each pixel to determine whether it belongs to the class “ice” or to the class “water.” From Eq. 1, it is clear that the detection of the ice pixels from water pixels is crucial to obtaining the ice concentration from an ice image.
The pixels in the same region have similar intensity. Based on that sea ice is whiter than open water, ice pixels have higher intensity values than those belonging to water in a uniform illumination ice image. Thus, the thresholding, which is based on the pixel’s gray-level to turn a grayscale image into a binary image (whose pixels have only two possible intensity values, e.g., “0” and “1”), is a natural way to segment ice regions from water regions.
Clustering is a statistical data analysis method that divides a data set into many groups (Basak et al. 1988), and it has been widely used in image segmentation, especially classifying the objects into many groups. This method is based on the mathematical distance measure between individual observations and groups of observations to find hidden structures in unlabeled data and assign the unlabeled data into groups, so that the data in one group are more similar to each other than to those in other groups.
Among various clustering algorithms, k-means is one of the simplest but most popular clustering algorithms. The goal of k-means clustering is to minimize the within-cluster sum of distance to partition a set of data into k clusters (MacQueen 1967). The step-by-step algorithm for this method in image segmentation is described below:
Iterate Steps 3 and 4 until the local means are unchanged.
Ice Floe Boundary Detection
In image processing, the detection of ice floe boundaries can be used to distinguish individual ice floes. With the individual ice floe identification result, ice floe characteristics, such as location, area, perimeter, and shape measurements of each ice floe, together with the floe size distribution can thereby be estimated. Thus, ice floe boundary detection is a vital for extracting information of ice floes from ice images.
In an actual ice-covered environment, especially in marginal ice zone (MIZ), ice floes typically touch each other, and the edges between touching floes may be difficult to identify in digital images. This issue significantly affects the analysis of individual ice floe properties and floe size distribution. Among various floe boundary detection methods, for example, derivative boundary detection (Zhang et al. 2012a, b), morphology-based method (Zhang et al. 2012a, b; Banfield 1991; Banfield and Raftery 1992), watershed-based algorithms (Blunt et al. 2012; Zhang et al. 2013), etc., the GVF (gradient vector flow) snake-based approach (Zhang et al. 2015; Zhang and Skjetne 2015) is the most advanced method nowadays.
Step 1: Convert the ice image into binary image after separating the ice from the water, in which case the pixels with value “1” indicate ice and pixels with value “0” indicate water; see Fig. 1a.
Step 2: Perform the distance transform to the binary image, and find the regional maxima shown as the green numerals in Fig. 1b.
Step 3: Merge the regional maxima into a big one if they have a short distance to each other, and then find the “seeds” that are centers of the regional maxima (including the merged ones), shown as red “+” in Figs. 1b and 2b.
After initializing the contours, the GVF snake algorithm is run on each contour to detect the floe boundary. By superimposing all the detected boundaries over the binarized ice image, it allows to separate touching ice floes and thereby be able to identify individual floes, as shown in Fig. 2.
Floe Shape Enhancement
Some segmented floes may contain holes or smaller ice floes inside after boundary detection, and the shape of the segmented ice floe is rough. To smoothen the shape of the ice floe, morphological cleaning is used after ice floe boundary detection.
The morphological opening is the union of all the translations of B that fit entirely within A. It can remove complete regions of an object that cannot contain the structuring element, smooths object contours, breaks thin connections, and removes thin protrusions as shown in Fig. 3c.
Step 1: Arrange all the segmented ice floes from small to large.
Step 2: Perform the morphological cleaning to the arranged ice floes in sequence.
Applications in Arctic Offshore Engineering
Model Ice Image Processing Applications
Before performing an analysis at full scale, the dynamic positioning (DP) experiments in model ice at the Hamburg Ship Model Basin (HSVA) in May 2011 allow for the testing of relevant image processing algorithms. This section shows the applications of the image processing techniques for determining important ice parameters from model ice data in the model-scale ice–structure analysis.
Managed ice conditions in the test runs, target values (full scale)
Floe size 1 (45%) [m]
Floe size 2 (40%) [m]
Floe size 3 (15%) [m]
Ice concentrations derived from different methods
Target value (%)
Run no. 5100
Run no. 5200
Average IC after reaching saturation in all test runs
Start time (s)
Model Ice Floe Monitoring
The ice floe area is less than a given threshold.
The ice floe has a convex shape (it could be done by determining if the ratio between the floe area and its minimum bounding polygon area is larger than the threshold).
The length-to-width ratio of the minimum bounding rectangle of the ice floe is less than the threshold.
Note that these criteria are designed for segmenting the rectangular-shaped crowded model ice floes only. For crowded model ice floes with other shapes, the criteria can be replaced by the corresponding shape criteria.
After a segmentation step, the algorithm will stop if all the segmented floes satisfy these criteria. Otherwise, the algorithm must find the floes that do not satisfy any of these criteria, find their seeds, initialize new contours, and perform the segmentation again (e.g., seen in Fig. 11d, e). After several segmentation steps, some segmented floes may still not satisfy the criteria. This is mainly because the boundaries of those floes are too weak to be detected. However, the total number of segmented floes will converge to a final solution. Therefore, the algorithm is made to stop if the total number of floes segmented after steps N and N + 1 are equal, in combination with an absolute stop criterion.
Sea Ice Image Processing Applications
The Norwegian University of Science and Technology (NTNU) expedition Oden Arctic Technology Research Cruise 2015 (OATRC’15) was carried out in the Arctic region in September 2015. During the research cruise, one of the helicopter flight missions was to capture ice conditions in the marginal ice zone (MIZ). The images contain valuable ice information in the MIZ. In this section, several methods are sequentially presented with an example to demonstrate an automated procedure for ice floe and brash ice identification, their numerical representation, and forthcoming ice field generation.
Sea Ice Image Processing
Sea Ice Numerical Modeling
Based on the image processing results, the identified ice floes and brash ice pieces are further simplified for the numerical simulation of the ice–structure interaction. In this modification, each sea ice floe is represented by a bounding polygon, and the brash ice pieces were reshaped by circular disks of equivalent area (Zhang 2015).
Ice Field Generation
The numerical representation of sea ice is utilized to generate its corresponding ice field to bridge the gap between a natural ice field and its numerical applications, e.g., simulations involving ice–structure interactions. A major challenge of utilizing the digitalized ice field is overlaps among ice floes and brash ice. These overlaps are the consequence of both the input image’s visual noise (e.g., the foggy bottom-left corner) and inaccuracies introduced by the adopted image processing technique (e.g., the procedure to polygonize the segmented ice floes). A non-smooth discrete element method (DEM) is adopted to resolve all the overlaps among different bodies and assign basic physics to each ice floe and brash ice (Zhang and Skjetne 2018; Zhang 2020).
Figure 19a shows that ice floes in the ice field’s bottom-left corner have more overlaps. This is mainly because of the input image’s visual noises. Nevertheless, applying the above non-smooth DEM calculation procedures, all the overlaps are eventually resolved in Fig. 19c, and its corresponding final ice field is shown in Fig. 19d. After resolving the overlaps, the exact location of each ice floe in Figs. 19d and 17a is not the same, but with only minor differences. On the other hand, each ice floe’s shape and size and the overall ice field’s ice mass are conserved.
Similarly, brash ice can be imported into the same non-smooth DEM-based simulator and be treated as discrete bodies. From a non-smooth DEM calculation’s point of view, the simplification of each brash ice as a disk with equivalent area makes the collision detection and consequent collision response calculation much easier comparing to arbitrary polygons. Given the amount of brash ice and its relatively small mass, this simplification is reasonable and has been adopted in previous studies (Konno 2009; Konno et al. 2011, 2013).
It is computationally efficient to make the circular disk-shaped simplification for brash ices. For the current ice field composition, i.e., 58.00% ice floe and 4.85% brash ice, the calculation time to resolve all overlaps for the cases with and without brash ice poses no significant difference. In both cases, the bottleneck for calculation time is on the overlap resolution in the bottom-left corner’s large ice floes. However, it is expected that as the amount of brash ice increases, the calculation time would also increase, which eventually becomes the decisive bottleneck for the calculation. To certain point, it might be more efficient to model brash ice as a continuum, e.g., a viscous flow, which is governed by conservation laws as a material collection.
Various image processing techniques have been introduced in this entry to extract useful ice information from the collected ice image data to support the estimation of ice forces that are critical to marine operations in the Arctic. The introduced methods have been applied to both model and sea ice image data to give some results applicable for ice engineering. More results and better information of ice from visual images will be investigated by further development of these image processing techniques.
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