High-Throughput, Resource-Efficient Multi-Dimensional Parallel Architecture for Space-Borne Sea-Land Segmentation

Abstract

Sea-land segmentation based on edge detection is commonly utilized in ship detection, coastline extraction, and satellite system applications due to its high accuracy and rapid speed. Pixel-level distribution statistics do not currently satisfy the requirements for high-resolution, large-scale remote sensing image processing. To address the above problem, in this paper, we propose a high-throughput hardware architecture for sea-land segmentation based on multi-dimensional parallel characteristics. The proposed architecture is well suited to wide remote sensing images. Efficient multi-dimensional block level statistics allow for relatively infrequent pixel-level memory access; a boundary block tracking process replaces the whole-image scanning process, markedly enhancing efficiency. The tracking efficiency is further improved by a convenient two-step scanning strategy that feeds back the path state in a timely manner for a large number of blocks in the same direction appearing in the algorithm. The proposed architecture was deployed on Xilinx Virtex k7-410t to find that its practical processing time for a [Formula: see text] remote sensing image is only about 0.4[Formula: see text]s. The peak performance is 1.625[Formula: see text]gbps, which is higher than other FPGA implementations of segmentation algorithms. The proposed structure is highly competitive in processing wide remote sensing images.