Infrared Small Target Detection In New Detection Systems

With the development of aerospace technology and the improvement of sensor performance,linear array of detectors and staring array of detectors have been under development in recent years.The scanners in linear array of detectors are designed from a single scan line to multiple scan lines.The staring array of detectors is designed from small to large format staring array.However,new small target detection methods are needed to match these new detection systems.This paper proposed three methods to detect small infrared targets in single frame,image sequences with high frame rate and image pairs with only two frames.The main work and research achievements are as follows:In Chapter 2,considering the different characteristics between target and background,we analyze 6 kinds of single small target detection methods,4 representative multi-frame infrared small target detections based on temporal profile and 2 other spatial and temporal small target detections which can be implemented in few infrared images.We analyze the advantages,disadvantages and the adaptability of these representative methods.It is the basis for the subsequent chapters of this paper.In Chapter 3,considering the problem of information deficiency faced by a single measure for single small target detection,we proposed to use both gray difference and variance difference measures to enhance infrared small targets and suppress background.First,a multi-scale adaptive gray difference measure is first used to enhance small targets and improve detection accuracy.Then,a multi-scale variance difference measure is proposed to alleviate the impact of background fluctuation and improve the robustness of our method.By integrating these two approaches,targets can be extracted accurately using a threshold-adaptive segmentation.Extensive experiments have been conducted on datasets with various scenes.Results have demonstrated the effectiveness and outperformance of our method as compared to the state-of-the-art methods.In Chapter 4,considering targets may presence sub-pixel motion characteristics in high frame rate image sequences,we employ a temporal profile method to detect theses targets.A multi-scale patch-based contrast measure is first used to suppress background and remove cloud edges at a coarse level.Then,a temporal variance filter is used to remove small broken cloud regions and suppress noise at a fine level.By integrating these two methods,we construct a low false alarm spatial and temporal filter for infrared small target detection.Then,infrared small targets can be extracted accurately and robustly using an adaptive-threshold segmentation.Experimental results indicate that our proposed method can robustly detect small infrared targets with a low false alarm rate.In Chapter 5,we first focus on mathematical modeling of a new double linear array detector.Once the detector scans from the up to the bottom,image pairs are generated at short intervals.After registration and removal of dynamic cloud edges in each image,the image differentiation based change detection method in temporal domain is proposed to combine with a structure tensor edge suppression method in spatial domain.Finally,experiments are conducted and our results are compared with theoretic analyses.Experimental results show that the proposed cloud edge removal solution can be used to successfully detect targets with a very low false alarm rate and an acceptable detection rate.Besides,we analyze the effects of spatial resolution and inter-frame registration accuracy on small target detection.It is a good foundation for the optimal design of double linear array detectors in future.