Satellite remote sensing image target matching recognition exhibits poor robustness and accuracy because of the unfit feature\nextractor and large data quantity. To address this problem, we propose a new feature extraction algorithm for fast target matching\nrecognition that comprises an improved feature from accelerated segment test (FAST) feature detector and a binary fast retina key\npoint (FREAK) feature descriptor. To improve robustness, we extend the FAST feature detector by applying scale space theory and\nthen transformthe feature vector acquired by the FREAK descriptor fromdecimal into binary.We reduce the quantity of data in the\ncomputer and improve matching accuracy by using the binary space. Simulation test results show that our algorithm outperforms\nother relevant methods in terms of robustness and accuracy.
Loading....