PID Controllers Guided Multitask Sea Ice Inversion Approach of SAR and Amsr-2 Images Based on Convolutional Neural Network

Polar sea ice monitoring is essential for climate change analysis and ship navigation route security. The current urgent problem to be solved is how to build a unified sea ice parameter inversion framework to obtain abundant and robust ice charts. However, most machine learning methods are merely suitable for modeling sea ice classification tasks under a single data source. Thus, a multitask sea ice inversion approach is proposed to address this challenge. First, SAR data and microwave scanning radiometer-2 (AMSR-2) data are jointly employed for multitask inversion, e.g., sea ice concentration (SIC), the stages of sea ice development (SOD), and sea ice floe size (FLOE). Then, the proportional-integral-derivative (PID) controllers are introduced into a three branch convolutional neural network to parse detailed, context and boundary information of the sea ice scene, respectively. Finally, three classifiers assign the multilabels to each of tasks for the parameter inversion. Experiments conducted on the AI4Arctic Sea Ice dataset show that our proposal outperforms typical CNN-based methods.