Multi-Task Deep Learning for Satellite Image Pansharpening and Segmentation

Andrew Khalel, Onur Tasar, Guillaume Charpiat, Yuliya Tarabalka

Abstract: In this work, we propose a novel multi-task framework, to learn satellite image pansharpening and segmentation jointly. Our framework is based on the encoder-decoder architecture, where both tasks share the same encoder but each one has its own decoder. We compare our framework against single-task models with different architectures. Results show that our framework outperforms all other approaches in both tasks.