Mr. Lee W. Cooper Profile

Lee W. Cooper

at Univ of Maryland Ctr for Environmental Science

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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.

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