NABLA-N for meltpond detection

Sultana, Aqsa; Asari, Vijayan K.; Aspiras, Theus; Liu, Ruixu; Sudakow, Ivan and Cooper, Lee W. (2024). NABLA-N for meltpond detection. In: Pattern Recognition and Prediction XXXV (Alam, Mohammad S. and Asari, Vijayan K. eds.), SPIE, 13040, article no. 1304006.

DOI: https://doi.org/10.1117/12.3016570

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).