Pattern recognition (psychology)

Gene

Algorithm

Data mining

Pixel

Artificial intelligence

Chemistry

Object detection

Contrast (vision)

False alarm

Measure (data warehouse)

Computer science

Computer vision

Biochemistry

Robustness (evolution)

Constant false alarm rate

IEEE Geoscience and Remote Sensing Letters

Infrared (IR) small target detection with high detection rate, low false alarm rate, and high detection speed has a significant value, but it is usually very difficult since the small targets are usually very dim and may be easily drowned in different types of interferences. Current algorithms cannot effectively enhance real targets and suppress all the types of interferences simultaneously. In this letter, a multiscale detection algorithm utilizing the relative local contrast measure (RLCM) is proposed. It has a simple structure: first, the multiscale RLCM is calculated for each pixel of the raw IR image to enhance real targets and suppress all the types of interferences simultaneously; then, an adaptive threshold is applied to extract real targets. Experimental results show that the proposed algorithm can deal with different sizes of small targets under complex backgrounds and has a better effectiveness and robustness against existing algorithms. Besides, the proposed algorithm has the potential of parallel processing, which is very useful for improving the detection speed.

Infrared Small Target Detection Utilizing the Multiscale Relative Local Contrast Measure