NABLA-N for meltpond detection

Aqsa Sultana; Vijayan K. Asari; Theus Aspiras; Ruixu Liu; Ivan Sudakow; Lee W. Cooper

doi:10.1117/12.3016570

7 June 2024 NABLA-N for meltpond detection

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

Proceedings Volume 13040, Pattern Recognition and Prediction XXXV; 1304006 (2024) https://doi.org/10.1117/12.3016570
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

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).

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Aqsa Sultana, Vijayan K. Asari, Theus Aspiras, Ruixu Liu, Ivan Sudakow, and Lee W. Cooper "NABLA-N for meltpond detection ", Proc. SPIE 13040, Pattern Recognition and Prediction XXXV, 1304006 (7 June 2024); https://doi.org/10.1117/12.3016570

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