CISNet: Change information guided semantic segmentation network for automatic extraction of glacier calving fronts

Abstract

The movement of the glacier calving front indicates changes in the mass balance of the glacier and is crucial for analyzing trends in global sea level changes. The launch of a large number of remote-sensing satellites has led to the generation of massive number of images that have enabled the application of deep-learning-based methods. However, existing methods generally focus solely on individual images and do not explore the relationships between glacier images. Therefore, this study proposes a change information-guided semantic segmentation network (CISNet) to explore category semantic relationships in glacier images by linking semantic segmentation with change information extraction tasks. In CISNet, we established a dual-branch architecture consisting of semantic segmentation and change information extraction using a weight-shared feature extraction module. U-ConvNextV2 was developed to extract multi-scale features of different classes in glacier images by integrating a high-performance feature-extraction module with the UNet effective framework. Its multi-scale feature fusion architecture based on skip connections ensures accurate segmentation of glacier semantics. To explore the relationships between different images, a pairwise change information extraction branch was used to extract consistent and inconsistent relationships from any image pair. The global random matching strategy for constructing image pairs enhanced the ability of the network to extract the features of glaciers and oceans. To improve the integration of the semantic features and change information during the training phase, an adaptive joint loss was proposed to dynamically adjust the optimization process of the two branches. Extensive experiments were conducted using the latest publicly available large-scale CaFFe dataset to validate this method, and CISNet outperformed the state-of-the-art deep-learning methods with a mean distance error (MDE) of 398 ± 43 m. To further validate the ability of CISNet to generalize across glaciers and regions, we selected data from a glacier area as the training dataset and the rest as the test set to construct a challenging CaFFe-SI dataset. In the CaFFe-SI experiment, CISNet achieved the best MDE of 888 ± 21 m and demonstrated a comprehensive superiority across the other evaluation metrics.