High throughput hardware architecture of a MIMO-based sea land segmentation for on-orbit remote sensing image processing

Sea land segmentation is an essential part in remote sensing image processing, greatly reducing the interesting area. However, faced with the faster real-Time processing of remote sensing images, the SLS algorithm's throughputs is always constrained by the self complexity and the on-chip resources. A new method based on MIMO for balancing resource and throughputs is proposed to segment the sea and land via edge detection. Firstly, according to the situation that the input and output data rate is not equal, the principle of MIMO is introduced to redesign the edge detection structure to double the bandwidth. Second, multi-port cache brings storage resources, which have to be reused, and reduce resource usage. Finally, the parallel sliding widdows method is adopted to usd in piplines of image outputing. The experimental results show that, compared with the state-of-Art structure, this method performed better in hardware utilization and bandwidth.