Infrared small target detection in heavy sky scene clutter based on sparse representation

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.

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.

Infrared small target detection is a crucial and yet still is a difficult issue in aeronautic and astronautic applications. Sparse representation is an important mathematic tool and has been used extensively in image processing in recent years. Joint sparse representation is applied in dual-band infrared dim target detection in this paper. Firstly, according to the characters of dim targets in dual-band infrared images, 2-dimension Gaussian intensity model was used to construct target dictionary, then the dictionary was classified into different sub-classes according to different positions of Gaussian function’s center point in image block; The fact that dual-band small targets detection can use the same dictionary and the sparsity doesn’t lie in atom-level but in sub-class level was utilized, hence the detection of targets in dual-band infrared images was converted to be a joint dynamic sparse representation problem. And the dynamic active sets were used to describe the sparse constraint of coefficients. Two modified sparsity concentration index (SCI) criteria was proposed to evaluate whether targets exist in the images. In experiments, it shows that the proposed algorithm can achieve better detecting performance and dual-band detection is much more robust to noise compared with single-band detection. Moreover, the proposed method can be expanded to multi-spectrum small target detection.

Infrared small target tracking algorithm based on temporal-spatial structure sparse Bayesian estimation

The infrared image in the sea background usually contains random noise, sea waves, fish scale light and other clutter. Sea waves and fish scale light interference are similar to small infrared targets, so it is difficult to eliminate such interference and detect small infrared targets quickly and accurately. In this paper, a small target detection method based on non-local similarity is proposed. The interference is eliminated by using the class feature similarity of interference and the overall feature uniqueness of the small target. Firstly, this paper performs filtering to remove random noise, and the suspected small targets can be extracted by executing Top Hat filtering. Secondly, a multi-feature vector is constructed for all suspected targets. Finally, this paper analyzes the characteristics of different interference and executes clustering step by step. The wave edge interference and fish scale light interference can be eliminated in turn. Feature threshold is set based on the interference that has been identified to obtain the true small infrared target according to the uniqueness of the characteristics of the true target. The experimental results show that compared with other small target detection algorithms, the proposed algorithm can detect small infrared targets in complex sea background effectively.

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.

Detection of low observable small targets from a sequence of InfraRed (IR) images is an important research area for image processing applications, but the difficulties in detecting small targets arise from the fact that they are not easily discernable from the background clutter. In this paper, the author presents a detection method based on Wavelet and Higher-Order Cumulant Filter (WHOCF). Firstly, the proposed method suppresses the background clutter with wavelet filter, then uses a Cumulant-Based Adaptive Filter (CBAF) in the further processing of the wavelet sub-band, which can suppress noise and preserve the candidate targets information effectively, and improve the Signal to Noise Ratio (SNR) greatly. Finally, a sequence images processing method is used before detection to enhance the performance of the detector. The experimental results with real world image sequences illustrate the effectiveness of the proposed method.

Small infrared target detection is one of the key techniques in infrared searching and tracking applications. A novel small target detection method based on the complex filter bank is proposed in this paper. Firstly, a training sample dataset is built and the complex filter bank is utilized to extract the feature representation of each pixel. Then, the support vector machine is used to detect the small target. Finally, a simple adaptive segmentation method is adopted to segment the result image. To evaluate the performance of the proposed method, background clutter suppression and small target detection probability experiments are conducted over real infrared small targets in two image sequences. The experiment results show that the proposed method can not only suppress background clutter effectively, but also detect targets accurately with low false alarm rate.

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.

Entropy-based window selection for detecting dim and small infrared targets

Detection of dim small targets in infrared (IR) images with high reliability is very important in defence systems. In this study, a new method is introduced based on human visual system and saliency maps fusion to detect the small target in IR images with high reliability. By using the static and motion saliency maps fusion, emphasizing the obtained saliencies from one method to another and applying the information and benefits of all maps in the saliency map fusion, this method suppresses the background clutter and noise with high reliability, makes the target more prominent and finally increases the contrast among them. The experiments are carried out on some real-life data of IR images containing the moving target. The obtained results show the efficiency and robustness of the proposed method so that this method improves the target signal to the background noise and clutter and increases the contrast between them and also detects the small target in IR images with low false alarm rates and high reliability.

Dim and small infrared target fast detection guided by visual saliency

Many targets of interest provide only very small signature differences from the clutter background. The ability to detect these small difference targets should be improved by using data which is diverse in space, time, wavelength or some other observable. Target materials often differ from background materials in the variation of their reflectance or emittance with wavelength. A multispectral sensor is therefore considered as a means to improve detection of small signal targets. If this sensor operates in the thermal infrared, it will not need solar illumination and will be useful at night as well as during the day. An understanding of the phenomenology of the spectral properties of materials and an ability to model and simulate target and clutter signatures is needed to understand potential target detection performance from multispectral infrared sensor data. Spectral variations in material emittance are due to vibrational energy transitions in molecular bonds. The spectral emittances of many materials of interest have been measured. Examples are vegetation, soil, construction and road materials, and paints. A multispectral infrared signature model has been developed which includes target and background temperature and emissivity, sky, sun, cloud and background irradiance, multiple reflection effects, path radiance, and atmospheric attenuation. This model can be used to predict multispectral infrared signatures for small signal targets.

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.