Abstract
Algorithms designed for ice-water classification of synthetic aperture radar (SAR) sea ice imagery produce only binary (ice and water) output typically using manually labelled samples for assessment. This is limiting because only a small subset of samples are used which, given the non-stationary nature of the ice and water classes, will likely not reflect the full scene. To address this, we implement a binary ice-water classification in a more informative manner taking into account the uncertainty associated with each pixel in the scene. To accomplish this, we have implemented a Bayesian convolutional neural network (CNN) with variational inference to produce both aleatoric (data-based) and epistemic (model-based) uncertainty. This valuable information provides feedback as to regions that have pixels more likely to be misclassified and provides improved scene interpretation. Testing was performed on a set of 21 RADARSAT-2 dual-polarization SAR scenes covering a region in the Beaufort Sea captured regularly from April to December. The model is validated by demonstrating; (i) a positive correlation between misclassification rate and model uncertainty and (ii) a higher uncertainty during the melt and freeze-up transition periods, which are more challenging to classify. Integration of the Iterative Region Growing with Semantics (IRGS) unsupervised segmentation algorithm with the Bayesian CNN outputs generates improved classification results, with notable improved results via visual inspection even when sample-based classification rates are similar.
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Chen, X., Scott, K.A., Clausi, D.A. (2023). Uncertainty Analysis of Sea Ice and Open Water Classification on SAR Imagery Using a Bayesian CNN. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13645. Springer, Cham. https://doi.org/10.1007/978-3-031-37731-0_26
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