An infrared dim and small target detection method based on fractional differential

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.

Small infrared moving target detection has an important role in the sea-based infrared search and tracking, maritime area surveillance and other applications. This method aims to detect the small infrared moving targets with the sea-sky background. The present study proposes a detection algorithm for small infrared targets based on the spatio-temporal saliency fusion. The contourlet analysis and edge extraction are carried out in the concurrent design. In order to effectively suppress the background and improve the target signal-clutter ratio, the spectral residual method is combined with the abovementioned methods to reconstruct the target fusion saliency image. Then the target motion region is estimated based on the optical flow method for the fusion saliency image and it is matched with the target area of interest to achieve the moving target detection. Moreover, the pipeline filtering is introduced to achieve the target confirmation by multi-frame judgment, reduce false alarm rate and complete the moving target detection for the infrared image sequence. Experimental results show that the proposed algorithm can achieve continuous target detection and have a higher detection precision via real long wave infrared image sequences.

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

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 traditional infrared small target detection method is easy to cause false alarms in the complex sea and sky background. In this letter, a fast detection method for infrared small targets in the complex sea and sky background is proposed. Firstly, in order to make full use of the Gaussian characteristics of infrared small targets and effectively distinguish the target from the background clutter, the SODD (second order directional derivative) filter is used to improve the target signal-to-clutter ratio (SCR) and suppress the background clutter; Secondly, in order to improve the real-time performance of the algorithm, we improve the existing MPCM algorithm by reducing the difference dimension between the target area and the local background area; Finally, we use pipeline filtering that can take full advantage of candidate target location, size, and total grayscale 3D information to complete multi-frame target validation and achieve continuous target detection in image sequences. The experimental results show that the proposed algorithm can stably and accurately detect infrared small targets in the infrared image sequence.

Aiming at the current situation of dim target detection in infrared image, a dim and small target detection method based on the most appropriate contrast saliency analysis was proposed. In sliding serial port, the non-linear processing technology was used to process the image, which avoided the saliency produced by traditional saliency analysis algorithm when processing the image at the scene edge. The problem of value interference does not affect the ability of target extraction in the target area. A large number of hardware-in-the-loop simulation experiments were carried out. The results show that although the proposed method can not improve significantly in the background suppression factor, the performance of target detection in the two indicators of mean signal-to-noise ratio and signal-to-noise ratio gain of the proposed method are significantly enhanced. And among the three methods of image processing, the effect is the best.

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

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.

: Sea clutter has a non-Rayleigh probability density when examined with a radar that has high resolution. This report discusses the properties of non- Rayleigh sea clutter and the detection of targets in non-Rayleigh sea clutter. The spatially varying clutter model is presented and is used to explain the non- Rayleigh nature of sea clutter. The variation of clutter distributions with various radar parameters such as frequency, pulse width, and polarization was determined, and the correlation properties of clutter were established. A way was developed of constructing a realistic realization of the sea surface on a computer, and the surface was used to predict non-Rayleigh clutter densities for various conditions and to indicate some finer points associated with the detection of small targets on the ocean's surface. Finally, various detectors were compared and the problems of pulse-to-pulse and scan-to-scan processing were analyzed.

Dim and small infrared target fast detection guided by visual saliency

This study focuses on advancing the field of remote sensing image target detection, addressing challenges such as small target detection, complex background handling, and dense target distribution. We propose solutions based on enhancing the YOLOv7 algorithm. Firstly, we improve the multi-scale feature enhancement (MFE) method of YOLOv7, enhancing its adaptability and precision in detecting small targets and complex backgrounds. Secondly, we design a modified YOLOv7 global information DP-MLP module to effectively capture and integrate global information, thereby improving target detection accuracy and robustness, especially in handling large-scale variations and complex scenes. Lastly, we explore a semi-supervised learning model (SSLM) target detection algorithm incorporating unlabeled data, leveraging information from unlabeled data to enhance the model’s generalization ability and performance. Experimental results demonstrate that despite the outstanding performance of YOLOv7, the mean average precision (MAP) can still be improved by 1.9%. Specifically, under testing on the TGRS-HRRSD-Dataset, the MFE and DP-MLP models achieve MAP values of 93.4% and 93.1%, respectively. Across the NWPU VHR-10 dataset, the three models achieve MAP values of 93.1%, 92.1%, and 92.2%, respectively. Significant improvements are observed across various metrics compared to the original model. This study enhances the adaptability, accuracy, and generalization of remote sensing image object detection.

The infrared (IR) small target detection algorithm with a high detection rate, low false alarm rate, and high real-time performance has significant application value in the field of IR remote sensing. IR small targets in complex backgrounds have low contrast and low signal-to-noise ratio (SNR). Therefore, small target detection is more difficult. Traditional IR small target detection is generally implemented by local contrast methods (LCM), nonlocal autocorrelation methods (NAM), and adaptive segmentation. In this letter, a robust infrared small target detection network (RISTDnet) is proposed based on deep learning. In RISTDnet, a feature extraction framework combining handcrafted feature methods and convolutional neural networks is constructed, a mapping network between feature maps and the likelihood of small targets in the image is established, and a threshold is applied on the likelihood map to segment real targets. Experimental results show that the RISTDnet can detect small targets with different sizes and low SNRs in complex backgrounds and have better effectiveness and robustness against existing algorithms.

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.

Robust and effective detection of a small target in an infrared maritime image is a key technology of maritime target search and tracking applications. Infrared small target detection is a challenging task due to the factors such as dim small targets and various complex backgrounds caused by sun glitters and strong waves. In this article, the integrated target saliency measure (ITSM) based on local and nonlocal spatial information is proposed to improve target detection performance. We combine local heterogeneity property of targets and nonlocal self-correlation property of background with targets' sparsity to separate real targets from background clutters. First, local heterogeneity calculation based on cross-window standard deviation (CSD) is proposed to extract candidate targets preliminarily, which enhances the local intensity difference between small targets and neighboring background. Meanwhile, low-rank representation (LRR) is applied to background prediction and removal, which is followed by adaptive threshold segmentation to enhance target saliency. Finally, we integrate the results obtained from the two steps mentioned above to further enhance targets and suppress background clutters. Then, real targets are extracted by an iterative threshold on the integrated map so as to generate the seed map, in addition, the target expansion strategy is exploited to keep full target areas. Experimental results on six datasets show that the proposed method outperforms the state-of-the-art methods in terms of robustness and target detection accuracy.

The main task of remote sensing image target detection is to locate and classify the targets of interest in remote sensing images, which plays an important role in intelligence investigation, disaster relief, industrial application, and other fields. However, the targets in the remote sensing image scene have special problems such as special perspective, scale diversity, multi-direction, small targets, and high background complexity. In this paper, the YOLOv5 target detection algorithm is improved according to the above characteristics. Aiming at the problem of large target size span in remote sensing images, this paper uses K-Means++ clustering algorithm to eliminate the problem in which the original clustering algorithm is sensitive to initial position, noise, and outliers, and optimizes the instability caused by K-Means clustering to obtain preset anchor frames. Aiming at the redundancy of background information around the location of remote sensing image targets, the large number of small targets, and the denseness of targets, a double IoU-aware decoupling head (DDH) is introduced at the output end to replace the coupled yolo head, which eliminates the interference caused by different task sharing parameters. At the same time, the correlation between positioning accuracy and classification accuracy is improved by the IoU-aware method. The attention mechanism is introduced into the backbone network to optimize the detection and small target detection in complex backgrounds. The mAP of the improved YOLOv5 algorithm is improved by 9%, and the detection effect of small targets and dense targets is significantly improved. At the same time, the paper has achieved good results through the verification of the DIOR remote sensing data set and has also achieved good performance advantages in comparison with other models.