A high-speed feature matching method of high-resolution aerial images

This paper presents a novel corner detection and scale estimation algorithm for image feature description and matching. Inspired by Adaboost’s weak classifier, a series of sub-detectors is elaborately designed to obtain reliable corner pixels. The new corner detection algorithm is more robust than the FAST and HARRIS algorithm, and it is especially suitable for the implementation in FPGA. The new scale estimation method can be directly implemented in the original image without building Gaussian pyramid and searching max response value in each level, which not only increase computational efficiency but also greatly reduces memory requirement. Based on the proposed algorithm, a CPU-FPGA cooperative parallel processing architecture is presented. The architecture overcomes the memory space limitation of FPGA and achieves high-speed feature matching for massive high-resolution aerial images. The speed of the CPU-FPGA cooperative process is hundred times faster than SIFT algorithm running on CPU, and dozens of times faster than SIFT running in CPU + GPU system.