Abstract
Satellite remote sensing image target matching recognition exhibits poor robustness and accuracy because of the unfit feature extractor and large data quantity. To address this problem, we propose a new feature extraction algorithm for fast target matching recognition that comprises an improved feature from accelerated segment test (FAST) feature detector and a binary fast retina key point (FREAK) feature descriptor. To improve robustness, we extend the FAST feature detector by applying scale space theory and then transform the feature vector acquired by the FREAK descriptor from decimal into binary. We reduce the quantity of data in the computer and improve matching accuracy by using the binary space. Simulation test results show that our algorithm outperforms other relevant methods in terms of robustness and accuracy.
Highlights
Matching recognition has many practical applications, including target recognition by matching target features, which is an important issue in the pattern recognition field
As a core step in satellite image target matching recognition, feature extraction may be classified into global and local invariant feature extraction based on the amount of utilised target information
We propose a new efficient method for satellite remote sensing image target matching recognition based on fast retina key point (FREAK) feature extraction algorithm
Summary
Matching recognition has many practical applications, including target recognition by matching target features, which is an important issue in the pattern recognition field. In [7], Hu invariant moment is applied to recognise an aircraft from a satellite image, and four features are extracted and combined to build the global invariant feature These methods assume that the edges of the target can be perfectly extracted, which is difficult to achieve in practice. Despite simulating the human vision system using a simple binary transform method to accelerate the matching process, the fast retina key point (FREAK) [14] descriptor has a relatively low accuracy. Given these limitations, we propose a new efficient method for satellite remote sensing image target matching recognition based on FREAK feature extraction algorithm.
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