Dr. Ivan Sudakow Profile

Dr. Ivan Sudakow

at The Open Univ.

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Publications (3)

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Proceedings Article | 7 June 2024 Presentation + Paper

NABLA-N for meltpond detection

Aqsa Sultana, Vijayan Asari, Theus Aspiras, Ruixu Liu, Ivan Sudakow, Lee Cooper

Proceedings Volume 13040, 1304006 (2024) https://doi.org/10.1117/12.3016570

KEYWORDS: Ice, Image segmentation, Education and training, Databases, Convolution, Performance modeling, Data modeling, Tunable filters, Solar radiation models, Solar radiation

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With increasing global temperatures due to anthropogenic climate change, seasonal sea ice in the Arctic has experienced rapid retreat, with increasing areal extent of meltponds that occur on the surface of retreating sea ice. Because meltponds have a much lower albedo than sea ice or snow, more solar radiation is absorbed by the underlying water, further accelerating the melting rate of sea ice. However, the dynamic nature of meltponds, which exhibit complex shapes and boundaries, makes manual analysis of their effects on underlying light and water temperatures tedious and taxing. Several classical image processing approaches have been extensively used for the detection of meltpond regions in the Arctic area. We propose a Convolutional Neural Network (CNN) based multiclass segmentation model termed NABLA-N (∇^N) for automated detection and segmentation of meltponds. The architectural framework of NABLA-N consists of an encoding unit and multiple decoding units that decode from several latent spaces. The fusion of multiple feature spaces in the decoding units enables better representation of features due to the combination of low and high-level feature maps. The proposed model is evaluated on high-resolution aerial photographs of Arctic sea ice obtained during the Healy-Oden Trans Arctic Expedition (HOTRAX) in 2005 and NASA’s Operation IceBridge DMS L1B Geolocated and Orthorectified image data in 2016. These images are classified into three classes: meltpond, open water and sea ice. We determined that NABLA-N demonstrates superior performance on segmentation of meltpond data compared to other state-of-the-art networks such as UNet and Recurrent Residual UNet (R2UNet).

Proceedings Article | 7 June 2024 Presentation + Paper

Diffusion model-based generation of sea ice data

Aqsa Sultana, Shaik Nordin Abouzahra, Vijayan Asari, Theus Aspiras, Ruixu Liu, Ivan Sudakow, Lee Cooper

Proceedings Volume 13033, 130330B (2024) https://doi.org/10.1117/12.3016574

KEYWORDS: Data modeling, Diffusion, Image processing, Ice, Education and training, Colorimetry, RGB color model, Model based design, Statistical modeling, Image segmentation

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As global warming causes climate change, extreme weather has become more common, posing a significant threat to life on Earth. One of the important indicators of climate change is the formation of melt ponds in the arctic region. Scarcity of large amount of annotated arctic sea ice data is a major challenge in training a deep learning model for the prediction of the dynamics of the melt ponds. In this research work, we use diffusion model, a class of generative models, to generate synthetic arctic sea ice data for further analysis of meltponds. Based on the training data, diffusion models can generate new and realistic data that are not present in the original dataset by focusing on the data distribution from a simple to a more complex distribution. First, simple distribution is transformed into a complex distribution by adding noise, such as a Gaussian distribution and through a series of invertible operations. Once trained, the model can generate new samples by starting from a simple distribution and diffusing it to the complex distribution, capturing the underlying features of the data. During inference, when generating new samples, the conditioning information is provided as input alongside the starting noise vector. This guides the diffusion process to produce samples that adhere to the specified conditions. We used high-resolution aerial photographs of Arctic region obtained during the Healy-Oden Trans Arctic Expedition (HOTRAX) in year 2005 and NASA’s Operation IceBridge DMS L1B Geolocated and Orthorectified data acquired in 2016 for the initial training of the generative model. The original image and synthetic image are assessed based on their chromatic similarity. We employed evaluation metric known as Chromatic Similarity Index (CSI) for the assessment purposes.

Proceedings Article | 13 June 2023 Presentation + Paper

R2UNet for melt pond detection

Aqsa Sultana, Vijayan Asari, Ivan Sudakow, Theus Aspiras, Ruixu Liu, Denis Demchev

Proceedings Volume 12527, 125270R (2023) https://doi.org/10.1117/12.2663982

KEYWORDS: Ice, Image segmentation, Machine learning, Semantics, Deep learning, Image processing, Image analysis

Read Abstract +

The massive shift in temperatures in the Arctic region has caused the increased Albedo effect as higher amount of solar energy is absorbed in the darker surface due to melting ice and snow. This continuous regional warming results in further melting of glaciers and loss of sea ice. Arctic melt ponds are important indicators of Arctic climate change. High-resolution aerial photographs are invaluable for identifying different sea ice features and are great source for validating, tuning, and improving climate models. Due to the complex shapes and unpredictable boundaries of melt ponds, it is extremely tedious, taxing, and time-consuming to manually analyze these remote sensing data that lead to the need for automatizing the technique. Deep learning is a powerful tool for semantic segmentation, and one of the most popular deep learning architectures for feature cascading and effective pixel classification is the UNet architecture. We introduce an automatic and robust technique to predict the bounding boxes for melt ponds using a Multiclass Recurrent Residual UNet (R2UNet) with UNet as a base model. R2UNet mainly consists of two important components in the architecture namely residual connection and recurrent block in each layer. The residual learning approach prevents vanishing gradients in deep networks by introducing shortcut connections, and the recurrent block, which provides a feedback connection in a loop, allows outputs of a layer to be influenced by subsequent inputs to the same layer. The algorithm is evaluated on Healy-Oden Trans Arctic Expedition (HO-TRAX) dataset containing melt ponds obtained during helicopter photography flights between 5 August and 30 September 2005. The testing and evaluation results show that R2UNet provides improved and superior performance when compared to UNet, Residual UNet (Res-UNet) and Recurrent U-Net (R-UNet).

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