An image preprocessing algorithm for infrared small target detection in the near-earth background

Yang, Yuanqing; Hou, Chuan-xun

doi:10.1117/12.2503043

Cited by 2 publications

(1 citation statement)

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“…The detection of dim and small targets can be broadly classified into single-framebased detection and sequence-based detection. Early common methods for detecting targets in single-frame images include median filtering [7], max-mean/max-median filter [8], bilateral filter [9], the Two-Dimensional Least Mean Square (TDLMS) algorithm [10], frequency domain transformation [11,12], and the top-hat algorithm based on morphological transformation [13], among others. In addition, certain methods that leverage sparsity and low rank have demonstrated notable outcomes.…”

Section: Introductionmentioning

confidence: 99%

Infrared Moving Small Target Detection Based on Space–Time Combination in Complex Scenes

Wang,

Cao,

et al. 2023

Remote Sensing

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In the infrared small target images with complex backgrounds, there exist various interferences that share similar characteristics with the target (such as building edges). The accurate detection of small targets is crucial in applications involving infrared search and tracking. However, traditional detection methods based on small target feature detection in a single frame image may result in higher error rates due to insufficient features. Therefore, in this paper, we propose an infrared moving object detection method that integrates spatio-temporal information. To address the limitations of single-frame detection, we introduce a temporal sequence of images to suppress false alarms caused by single-frame detection through analyzing motion features within the sequence. Firstly, based on spatial feature detection, we propose a multi-scale layered contrast feature (MLCF) filtering for preliminary target extraction. Secondly, we utilize the spatio-temporal context (STC) as a feature to track the image sequence point by point, obtaining global motion features. Statistical characteristics are calculated to obtain motion vector data that correspond to abnormal motion, enabling the accurate localization of moving targets. Finally, by combining spatial and temporal features, we determine the precise positions of the targets. The effectiveness of our method is evaluated using a real infrared dataset. Through analysis of the experimental results, our approach demonstrates stronger background suppression capabilities and lower false alarm rates compared to other existing methods. Moreover, our detection rate is similar or even superior to these algorithms, providing further evidence of the efficacy of our algorithm.

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