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The infrared small target detection technology has a wide range of applications in maritime defense warning and maritime border reconnaissance, especially in the maritime and sky scenes for detecting potential terrorist attacks and monitoring maritime borders. However, due to the weak nature of infrared targets and the presence of background interferences such as wave reflections and islands in maritime scenes, targets are easily submerged in the background, making small infrared targets hard to detect. We propose the multidimensional information fusion network(MIFNet) that can learn more information from limited data and achieve more accurate target segmentation. The multidimensional information fusion module calculates semantic information through the attention mechanism and fuses it with detailed information and edge information, enabling the network to achieve more accurate target position detection and avoid detecting one target as multiple ones, especially in high-precision scenes such as maritime target detection, thus effectively improving the accuracy and reliability of detection. Moreover, experiments on our constructed dataset for small infrared targets in maritime scenes demonstrate that our algorithm has advantages over other state-of-the-art algorithms, with an IoU of 79.09%, nIoU of 79.43%, F1 score of 87.88%, and AuC of 95.96%. [ABSTRACT FROM AUTHOR]

Highlights • A simple but effective spatial filter is proposed to obtain the SLCM. • An enhanced time difference method is proposed to obtain the TLCM. • The STLCM is defined by multiplying the SLCM and TLCM. • An enhanced threshold segmentation method is proposed. Abstract Infrared (IR) moving point target detection is an important technique in many onboard applications such as remote sensing, IR searching and tracking (IRST) and early warning system. However, it has been facing great challenges due to the complicated background and the limited processing resources in the onboard system. In this paper, a novel spatial-temporal local contrast method is proposed for moving point target detection in space-based IR imaging system. Firstly, a simple but effective spatial filter based on multi-direction filtering fusion is designed to obtain the spatial local contrast map (SLCM). An enhanced time domain difference method is also proposed to obtain the temporal local contrast map (TLCM). Then, the spatial–temporal local contrast map (STLCM) is obtained by multiplying the newly defined SLCM and TLCM. Finally, an enhanced threshold segmentation method is proposed for target detection and false alarm suppression. To verify the performance of our detection algorithm, we conduct several groups of experiments on four different real IR image sequences with simulated targets. The final experimental results show that our algorithm significantly outperforms other methods in terms of background suppression and target detection. [ABSTRACT FROM AUTHOR]

As one of important physical properties of exo-atmosphere objects, micro-motion dynamics provides extra information for objects discrimination. In this paper, a model and theoretical analysis of time sequence of infrared (IR) signature considering different inertial parameters of micro-motion objects are presented. It overcomes the shortcoming of the existing methods, which depend on the assumption of specific micro-motion types. Firstly, an inertial model of exo-atmosphere micro-motion object is constructed. Next, the relationship between inertial parameters and IR signature model is derived. Finally, power spectral density transformed from the time sequence of IR signature is obtained. Four groups of simulations are conducted demonstrating the effects of inertial parameters posed on IR signature. Simulation results show that inertial parameters including moments of inertia (MOI) and initial angular rate (IAR) determine the period of motion, thus the frequency of time-varying radiant intensity received by the sensor. The proposed method exploits more explicit physical meaning of exo-atmosphere micro-motion objects and provides a novel approach to characterize exo-atmosphere micro-motion objects irrespective of their specific micro-motion types, which exhibits a greater flexibility in IR objects discrimination tasks. [ABSTRACT FROM AUTHOR]