Small target detection using an optimization-based filter

Dim and small infrared target fast detection guided by visual saliency

Infrared small target detection is one of the most important parts of infrared search and tracking (IRST) system. Generally, the small and dim target is of low signal-to-noise ratio and buried in the complicated background and heavy noise, which makes it extremely difficult to be detected with low false alarm rates. To solve this problem, we propose a small target detection method based on multiscale local contrast measure. Different from conventional methods, we novelly measure the local contrast from two aspects: local dissimilarity and local brightness difference. First, we present a new dissimilarity measure called the local energy factor (LEF) to describe the dissimilarity between the small targets and their surrounding backgrounds. Second, the feature of the brightness difference between the small targets and the backgrounds is utilized. Afterward, the local contrast is measured by taking both features of the above into account. Finally, an adaptive segmentation method is applied to extract the small targets from the backgrounds. Extensive experiments on real test data set demonstrate that our approach outperforms the state-of-the-art approaches.

This paper extends the smoothing algorithm based on integrated probabilistic data association to track multiple maneuvering targets by applying smoothing to joint integrated probabilistic data association (JIPDA). The proposed algorithm utilizes smoothing data association to obtain smoothing prediction which is needed to calculate the smoothing data association probabilities, the smoothing target trajectory state estimates and the smoothing target existence probability. The smoothing data association probabilities are used to update and propagate the forward tracks for tracking multiple maneuvering targets in clutter. This algorithm is called fixed-interval smoothing JIPDA (JIPDAS). Simulation is carried out to show improved false track discrimination performance over the existing algorithms for tracking multiple maneuvering targets in a heavy cluttered environment.

This paper discusses the objectives of the Air Force's Infrared Search and Track (IRST) Advanced Development Program and the basic elements of an infrared system that must be analyzed prior to the design of an IRST system. Why Is An IRST Needed? The development and deploynient of IRST systems is essential to meet the critical needs of modern air-to-air warfare. These needs are: o Operation in jamming environment o Long-range detection The high-velocity targets at high altitude creates a severe time compression problem for successful intercept which imposes a need for long range detection. Because of the intense IR radiation of the high fast target, an Infrared Search Track System can provide this long-range detection capability. It performs this task passively, on both large and small radar cross-section targets, without being hindered by jamming. The prime features of the IRST against the low altitude tactical targets is again passive detection without degradation by jamming. IRST detection range at low altitude is much less than for the high altitude targets because the targets fly slower, radiate less IR energy and there is significantly more atmospheric attenuation. IRST Program Overview Infrared Search and Track Systems (IRSTS) are now under development by the Air Force (AFWALIAART-1). General Electric and ITT Avionics were awarded contracts in 1981 for the design, fabricatjon and flight test of an IRST system. Both IRST systems are packaged in a pod that will be mounted for flight test on the left forward inboard missile (AIM-7) location on the F-15 test aircraft. The flight test is a feasibility demonstration to determine the relative quantitative and qualitative effectiveness and utility of an IRST as a complement to an airborne intercept radar. The IRSTS will be evaluated under a wjde range of offset angles, relative velocities, ranges and altitudes, emphasizing realistic operational scenarios.

A pixel-level local contrast measure for infrared small target detection

Small and dim target detection is a critical challenge in hyperspectral remote sensing, particularly in complex, large-scale scenes where spectral variability across diverse land cover types complicates the detection process. In this paper, we propose a novel target reasoning algorithm named Adaptive Global Dense Nested Reasoning Network (AGDNR). This algorithm integrates spatial, spectral, and domain knowledge to enhance the detection accuracy of small and dim targets in large-scale environments and simultaneously enables reasoning about target categories. The proposed method involves three key innovations. Firstly, we develop a high-dimensional, multi-layer nested U-Net that facilitates cross-layer feature transfer, preserving high-level features of small and dim targets throughout the network. Secondly, we present a novel approach for computing physicochemical parameters, which enhances the spectral characteristics of targets while minimizing environmental interference. Thirdly, we construct a geographic knowledge graph that incorporates both target and environmental information, enabling global target reasoning and more effective detection of small targets across large-scale scenes. Experimental results on three challenging datasets show that our method outperforms state-of-the-art approaches in detection accuracy and achieves successful classification of different small targets. Consequently, the proposed method offers a robust solution for the precise detection of hyperspectral small targets in large-scale scenarios.

The ability to detect and track dim unresolved targets in heavy clutter can be improved by the inclusion of the spectral dimension. Because of the great variation in targets, operating conditions and environments factors the spectral signature of the target is typically unknown. This paper present a fully adaptive matched filter and tracking paradigm which assumes no a priori information about the spectral signature of the target. It is shown that the full SCR gain can be realized in the absence of the spectral signature of the target. The ROC curve of the detector is used to show that performance loss due to the absence of spectral information is entirely due to an increase in the false alarm probability. This increase in PFA adversely effects tracker performance. The SCR track feature is developed to mitigate these effects. Track features provide an information shunt around the detection threshold nonlinearity that would otherwise block the flow of useful information to the tracker.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Small infrared target detection using absolute average difference weighted by cumulative directional derivatives

With the rapid development of UAV and infrared imaging technology, the cost of UAV infrared imaging technology has decreased steadily. Small target detection technology in aerial infrared images has great potential for applications in many fields, especially in the field of traffic surveillance. Because of the low contrast and relatively limited feature information in infrared images compared to visible images, the difficulty involved in small road target detection in infrared aerial images has increased. To solve this problem, this study proposes a feature-enhanced attention and dual-GELAN net (FEADG-net) model. In this network model, the reliability and effectiveness of small target feature extraction is enhanced by a backbone network combined with low-frequency enhancement and a swin transformer. The multi-scale features of the target are fused using a dual-GELAN neck structure, and a detection head with the parameters of the auto-adjusted InnerIoU is constructed to improve the detection accuracy for small infrared targets. The viability of the method was proved using the HIT-UAV dataset and IRTS-AG dataset. According to a comparative experiment, the mAP50 of FEADG-net reached more than 90 percent, which was higher than that of any previous method and it met the real-time requirements. Finally, an ablation experiment was conducted to demonstrate that all three of the modules proposed in the method contributed to the improvement in the detection accuracy. This study not only designs a new algorithm for small road object detection in infrared remote sensing images from UAVs but also provides new ideas for small target detection in remote sensing images for other fields.

In the field of infrared remote sensing, the problem of IR small target detection is still an important component part. Concerning infrared dim small target (IRDST) detection, firstly the infrared image is processed with DWT method to get the wavelet coefficients image, but the distribution characteristics, such as scales, frequencies, orientations of wavelet coefficients in different sub-bands are various, so wavelet image denoised by a single threshold criterion can not give a satisfying estimation. Based on this motivation, a spatially adaptive multi-model de-noising strategy (SAMMDS) based IRDST detection method is proposed in this paper, which can adjust thresholding strategy according to the distribution of noise in different scales and directions. Spatially adaptive BayesShrink (SABS) thresholding, traditional BayesShrink (BS) thresholding and generalized cross validation (GCV) thresholding are all adopted here to process each sub-band separately. After reconstructing the denoised wavelet image, a simple global thresholding is used to separate the background and target finally. Experimental results demonstrate that the proposed algorithm performs better than other typical wavelet methods for small target detection with various complex backgrounds.

Research on Infrared Dim and Small Target Detection Algorithm Based on Low-Rank Tensor Recovery

Robust and effective detection of small target and false alarm (FA) suppression are the key techniques in infrared search and track systems. In this paper, the derivative entropy-based contrast measure (DECM) is proposed for small-target detection under various complex background clutters. Initially, different directional derivatives of an infrared image are calculated based on the facet model. Then, by analyzing the derivative properties of small target, the primitive entropy formula is improved by incorporating derivative information. With the improved entropy, the contrast measure is constructed to enhance small target and suppress background clutters in each derivative subband. Finally, the contrast measure maps derived from derivative subbands are fused together. The small target could be segmented easily from the fusion result. Experimental results demonstrate that DECM could effectively enhance dim small targets and suppress complex background clutters. Besides, DECM is also robust to infrared small-target images with noises of different levels. The detection results achieve higher detection ratio and lower FA compared with those of other methods under various infrared scenes.

Modern radar should be able to detect targets in heavy clutter and to identify specific targets. Both tasks require the ability to resolve close scatterers in order to be able to differentiate the target, either from natural or man-made objects. The output of a bat auditory system model for two close target echoes was studied. It was shown how the phase of the signal can be used to improve the resolution performance. The improvement is expected for ultra wideband bat-like waveforms and confirmed with experiments with ultrasound.

This paper presents a new small target detection method using scale invariant feature. Detecting small targets whose sizes are varying is very important to automatic target detection in infrared search and track (IRST). The conventional spatial filtering methods with fixed sized kernel show limited target detection performance for incoming targets. The scale invariant target detection can be defined as searching for maxima in the 3D (x, y, and scale) representation of an image with the Laplacian function. The scale invariant feature can detect different sizes of targets robustly. Experimental results with real FLIR images show higher detection rate and lower false alarm rate than conventional methods. Furthermore, the proposed method shows very low false alarms in scan-based IR images than conventional filters.

The main Infrared Search and Track systems (IRST) purpose is to realize optimal discrimination between true targets and background clutter (false alarm). In such single band systems, background prediction is frequently used for detecting small targets. However, detection performances are strongly influenced by background gurgitation. The method based on maximum background model can reduce this kind of influence. But present background prediction methods choose background pixels around the prediction pixel from every direction, as a result, background pixels around the target will be 'poisoned' by target, and contrast will be greatly reduced accordingly. Threshold chosen to detect the target in the predicted residual image will decrease, and this will result in too many false targets and increase false alarms. For the small targets detection in IR images, a method of background prediction based on multi-band background model is proposed. For the purpose of removing the target poison, an improved rule of selecting background pixels according to the certain spectral difference between the expected target and background has been developed in this method. The use of this information is based on theoretical spectral radiance discrimination in LWIR and MWIR bands, between targets and backgrounds. When the current spectral parameter matches spectral background response, the current pixel is judged as a background pixel, and involve in background prediction operation, otherwise, it is judged as a target pixel, and will not involve in this operation. The multi-band background model, which improves the performance of small targets detection, eliminates the effect of target on the background prediction, achieves more accurate prediction of background, and increases the contrast of target and background. This is a significant development to the background prediction algorithm by extending to multi-band domain. Simulation results validate the effectiveness of the algorithm in this paper.