High Level Synthesis Acceleration of Change Detection in Multi-Temporal High Resolution Sentinel-2 Satellite Images

Machine learning tools are at the spotlight of research and human scientific activities that perform image processing and object detection. The Earth observation domain in particular heavily relies on change detection on images employing image segmentation techniques and a variety of prediction models. In this paper, we focus on such an application, that performs change detection on multi-temporal Sentinel-2 satellite images. The goal of this work is to explore High Level Synthesis capabilities of Intel OpenCL SDK to produce an efficient architecture for accelerating the applications focusing on optimization of a single prediction while taking into account the fragmentation of the problem. Our two-level approach first employs built-in optimization techniques to impact microarchitectural attributes and then scales this baseline to leverage coarse-grain and fine-grained parallelism. The result for the fastest implementation we acquire is a speedup of ×7.14 over the Python-TF2 implementation.