Dim target detection in IR image sequences based on fractal and rough set theory

Xiaoke Yan; Caicheng Shi; Peikun He

doi:10.1117/12.658169

4 January 2006 Dim target detection in IR image sequences based on fractal and rough set theory

Xiaoke Yan, Caicheng Shi, Peikun He

Proceedings Volume 5985, International Conference on Space Information Technology; 59853K (2006) https://doi.org/10.1117/12.658169
Event: International Conference on Space information Technology, 2005, Wuhan, China

Abstract

This paper addresses the problem of detecting small, moving, low amplitude in image sequences that also contain moving nuisance objects and background noise. Rough sets (RS) theory is applied in similarity relation instead of equivalence relation to solve clustering issue. We propose fractal-based texture analysis to describe texture coarseness and locally adaptive threshold technique to seek latent object point. Finally, according to temporal and spatial correlations between different frames, the singular points can be filtered. We demonstrate the effectiveness of the technique by applying it to real infrared image sequences containing targets of opportunity and evolving cloud clutter. The experimental results show that the algorithm can effectively increase detection probability and has robustness.

Citation Download Citation

Xiaoke Yan, Caicheng Shi, and Peikun He "Dim target detection in IR image sequences based on fractal and rough set theory", Proc. SPIE 5985, International Conference on Space Information Technology, 59853K (4 January 2006); https://doi.org/10.1117/12.658169

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KEYWORDS

Fractal analysis

Target detection

Detection and tracking algorithms

Infrared imaging

Clouds

Infrared detectors

Linear filtering

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