Infrared small target detection (ISTD) is a fundamental component of infrared search and tracking systems. The low-rank sparse decomposition method has become the mainstream of ISTD due to its broad applicability across various scenarios. However, certain sparse interferences in complex backgrounds may limit the effectiveness of these methods. To solve this problem, we propose a non-local affinity-based robust interference-resistant model (NARIRM) for ISTD. The model leverages the concept of affinity, which denotes the relationship between pixel regions, assuming that interference has stronger affinity with its neighbors than the target. The affinity values are achieved by reformulating an infrared image as the linear combination of foreground and background and using sparse decomposition results as constraints. A suppressor is then derived to reduce the impact of sparse interference by the affinity values. Experimental evaluation on on public datasets demonstrates the proposed method outperforms several state-of-the-art techniques. The code is available at https://github.com/djk1997-jk/NARIRM.

Infrared small target detection (ISTD) is widely used in civilian and military applications. However, ISTD encounters several challenges, including the tendency for small and dim targets to be obscured by complex backgrounds. To address this issue, we propose the Dynamic Attention Transformer Network (DATransNet), which aims to extract and preserve detailed information vital for small targets. DATransNet employs the Dynamic Attention Transformer (DATrans), simulating central difference convolutions (CDC) to extract gradient features. Furthermore, we propose a global feature extraction module (GFEM) that offers a comprehensive perspective to prevent the network from focusing solely on details while neglecting the global information. We compare the network with state-of-the-art (SOTA) approaches and demonstrate that our method performs effectively. Our source code is available at https://github.com/greekinRoma/DATransNet.

Infrared small target detection (ISTD) plays a critical role in a wide range of civilian and military applications. Existing methods suffer from deficiencies in the localization of dim targets and the perception of contour information under dense clutter environments, severely limiting their detection performance. To tackle these issues, we propose a contour-aware and saliency priors embedding network (CSPENet) for ISTD. We first design a surround-convergent prior extraction module (SCPEM) that effectively captures the intrinsic characteristic of target contour pixel gradients converging toward their center. This module concurrently extracts two collaborative priors: a boosted saliency prior for accurate target localization and multi-scale structural priors for comprehensively enriching contour detail representation. Building upon this, we propose a dual-branch priors embedding architecture (DBPEA) that establishes differentiated feature fusion pathways, embedding these two priors at optimal network positions to achieve performance enhancement. Finally, we develop an attention-guided feature enhancement module (AGFEM) to refine feature representations and improve saliency estimation accuracy. Experimental results on public datasets NUDT-SIRST, IRSTD-1k, and NUAA-SIRST demonstrate that our CSPENet outperforms other state-of-the-art methods in detection performance. The code is available at https://github.com/IDIP-Lab/CSPENet.

Infrared ship detection (IRSD) is crucial for numerous applications but faces challenges, such as small targets and complex backgrounds, resulting in misdetections and false alarms. In order to address these challenges, we propose the scene semantic prior-assisted infrared ship detection using multitask perception network (SMPISD-MTPNet). This network employs multitask perception: one task is to predict targets, and the other focuses on scene perception to suppress false alarms caused by background interference. To highlight dim and small targets, we use the scene semantic extractor (SSE) to guide the network using features extracted based on expert knowledge and the gradient-based module to enhance the edge and point features. We apply data augmentation to the networks and employ a training trick called soft fine-tuning to improve the network’s generalization and suppress the distortion caused by the augmentation process. Due to the unavailability of datasets with appropriate scene labels for scene perception, we have developed a new dataset called the infrared ship dataset with scene segmentation (IRSDSS). In addition, we have enhanced an existing dataset by adding scene masks and created the enhanced infrared ship detection dataset (EISDD). Our evaluations using both IRSDSS and EISDD demonstrate that SMPISD-MTPNet exceeds contemporary state-of-the-art (SOTA) methods in accuracy. The source code and dataset for this research can be available at:https://github.com/greekinRoma/SMPISD-MTPNet.

Infrared small target detection (ISTD) boasts extensive applications across civil and military domains, owing to its exceptional all-day performance. Neural network innovations have led to deep ISTD models that achieve heightened accuracy through extensive datasets. However, these general networks often fail to perceive the sensitivity of small targets and adopt heavy constructions to preserve potential target features, neglecting domain-specific insights and suffering from poor explainability. Our work seeks to rectify this by revisiting the saliency principles inherent to ISTD and developing a learnable local saliency kernel network (L2SKNet). This approach implements a learnable local saliency kernel module (LLSKM) that embodies the concept of “Center subtracts Neighbors,” guiding the network to capture the saliency features (points or edges). We enhance LLSKM by incorporating strategic dilation and structuring it hierarchically, which boosts its capability to capture multiscale infrared features while avoiding parameter explosion. In pursuit of efficiency, we also refine LLSKM into a more compact form by factorizing it into two orthogonal 1-D kernels, yielding a lightweight version. Heatmap visualizations and rigorous quantitative analyses corroborate the effectiveness of our local saliency-guided networks. Comprehensive testing reveals that L2SKNet variants outperform established baselines, demonstrating significant improvements in both visual and numerical assessments. The code is available athttps://github.com/fengyiwu98/L2SKNet.

Infrared small target detection plays a vital role in remote sensing, industrial monitoring, and various civilian applications. Despite recent progress powered by deep learning, many end-to-end convolutional models tend to pursue performance by stacking increasingly complex architectures, often at the expense of interpretability, parameter efficiency, and generalization. These models typically overlook the intrinsic sparsity prior of infrared small targets–an essential cue that can be explicitly modeled for both performance and efficiency gains. To address this, we revisit the model-based paradigm of Robust Principal Component Analysis (RPCA) and propose Dynamic RPCA Network (DRPCA-Net), a novel deep unfolding network that integrates the sparsity-aware prior into a learnable architecture. Unlike conventional deep unfolding methods that rely on static, globally learned parameters, DRPCA-Net introduces a dynamic unfolding mechanism via a lightweight hypernetwork. This design enables the model to adaptively generate iteration-wise parameters conditioned on the input scene, thereby enhancing its robustness and generalization across diverse backgrounds. Furthermore, we design a Dynamic Residual Group (DRG) module to better capture contextual variations within the background, leading to more accurate low-rank estimation and improved separation of small targets. Extensive experiments on multiple public infrared datasets demonstrate that DRPCA-Net significantly outperforms existing state-of-the-art methods in detection accuracy. Code is available at https://github.com/GrokCV/DRPCA-Net.

Deep learning (DL) networks have achieved remarkable performance in infrared small target detection (ISTD). However, these structures exhibit a deficiency in interpretability and are widely regarded as black boxes, as they disregard domain knowledge in ISTD. To alleviate this issue, this work proposes an interpretable deep network for detecting infrared dim targets, dubbed RPCANet. Specifically, our approach formulates the ISTD task as sparse target extraction, low-rank background estimation, and image reconstruction in a relaxed Robust Principle Component Analysis (RPCA) model. By unfolding the iterative optimization updating steps into a deep-learning framework, time-consuming and complex matrix calculations are replaced by theory-guided neural networks. RPCANet detects targets with clear interpretability and preserves the intrinsic image feature, instead of directly transforming the detection task into a matrix decomposition problem. Extensive experiments substantiate the effectiveness of our deep unfolding framework and demonstrate its trustworthy results, surpassing baseline methods in both qualitative and quantitative evaluations. Our source code is available at https://github.com/fengyiwu98/RPCANet.

Due to the lack of color and texture information and the fuzzy boundary of infrared (IR) small targets, the pixel-level mask annotation process consumes a lot of manual cost and is difficult to achieve accurate annotation. To further reduce the annotation burden, we propose an IR small target mask generation algorithm based on single-point supervised multi-scale chain growth clustering (MCGC). The core of this work is the adaptive generation of IR small-target pseudo mask maps under the supervision of randomly given single-point labels, sequentially through the strategies of multi-scale chain growth, Euclidean coefficient decay, K-Means clustering, and eight-neighborhood clustering. On the four public datasets, ablation experiments, qualitative and quantitative comparison experiments demonstrate that the MCGC algorithm has an efficient and accurate IR small target pseudo mask generation capability, which can be adapted to different numbers, scales, shapes, and intensities of targets in complex backgrounds. In addition, IR-Labelmask, an IR small target mask annotation software designed based on the MCGC algorithm, is publicly available on kourenke/IR-Labelmask-software (github.com). To our knowledge, this is the first mask annotation software designed for IR small target.

High-resolution remote sensing image change detection focuses on ground surface changes. It has wide applications, including territorial spatial planning, urban region detection, and military operations. However, class imbalance and pseudo-changes are caused by the unchanged areas far outnumbering the changed areas and lighting changes. To address these problems, we propose spatial-temporal attention with a difference enhancement-based network (STADE-CDNet). In STADE-CDNet, a change detection difference enhancement module (CDDM) is proposed to extract important features from the difference map to detect changed regions. This module enhances the network with differential feature attributes through the training layer, improving the network’s learning ability and reducing the imbalance problem. A temporal memory module (TMM) is designed to extract temporal and spatial information. Inspired by the self-attention mechanism of the transformer, we propose a transformer and TMM (TTMM). Four encoding layers are designed to detect the semantic information from high to low levels of the multitemporal image pairs. The fusion and parallelism of multivariate data are achieved through collaborative modeling of deep learning and change detection, compensating for the need for excessive human intervention in traditional algorithms. We evaluate our approach in two different datasets (LEVIR-CD and DSIFN-CD). Promising quantitative and qualitative results show that STADE-CDNet can improve accuracy. In particular, the proposed CDDM significantly reduces false positive detection, with F1 scores at least 1.97% and 2.1% higher than other methods in the case of the LEVIR-CD and DSIFN-CD datasets, respectively. Our code is available at https://github.com/LiLisaZhi/STADE-CDNet.

This paper proposes parallel peak filters to reject multi-frequency disturbances beyond the baseline feedback control bandwidth, which is restricted by time delays of the sample-data control system. Supplementing the baseline feedback control with parallel peak filters has an extremely simple control structure. In addition, a time-delay compensation method is proposed to eliminate the adverse effect of time delay on the designing parallel peak filters. Such deign break through the bandwidth limitation of baseline feedback control and provide a new idea for disturbance rejection beyond the bandwidth. Furthermore, multiple parallel peak filters with time-delay compensation provide multi-frequency disturbance rejection, and each parallel peak filter is independent of each other. Experiments are conducted in an image-based stabilization system, and the experimental results demonstrate that the proposed method can enhance multi-frequency disturbance rejection beyond the baseline feedback control bandwidth in time-delay systems.

Remote sensing image change detection (CD) is used to identify ground surface changes by analyzing multi-temporal images. Scholars have summarized and extended numerous useful methods for high-resolution remote sensing images. Most of the existing approaches use clustering and correction to detect change regions. However, some methods cannot detect change areas accurately due to bountiful clutters and complex information in high-resolution remote sensing images. To overcome these limitations, we propose a new detection approach using image-inverse correction and density peak clustering: Firstly, the multi-temporal images are corrected by the relative geometry and intensity. Then, the positive and inverse phase difference images are calculated.Moreover, the V channel is extracted in the HSV color space. Subsequently, applying image-inverse correction enhances the intensity of change regions and suppresses unchanged regions. Furthermore, density peak clustering is implemented to remove clutters. Finally, the processed positive-phase differential images are fused with the inverse-phase differential images to obtain the final change detection results. This approach establishes an accurate and efficient module to detect the change areas, which can accurately detect the change regions in the multi-temporal remote sensing image. The experiments show that the proposed research has stronger detection accuracy than the other six advanced change detection algorithms.

Infrared small target detection (ISTD) technique is widely used in infrared searching and tracking (IRST) and military surveillance. Existing detection methods must sufficiently address the challenges of the heterogeneous background with high concealment targets. In this letter, we propose a novel spatial-temporal tensor ring norm regularization (STT-TRNR) to detect infrared small targets. First, to utilize the spatial and temporal context information in a sequence, nonrepetitive spatial-temporal patches are formed by sliding windows in the consecutive frames. The patches are stacked into a tensor structure with spatial and temporal information. Second, the tensor ring nuclear norm is introduced to approximate the rank of the background tensor. The tensor ring regularization improves the correlation between dimensions, protects the internal structure of the tensor, and avoids the dimension disaster caused by train decomposition. Third, the local contrastive feature is used as a priori information to suppress the false alarms caused by the corner edges and other noises and avoid the distortion caused by target movement. Finally, the alternating direction multiplier method (ADMM) is employed to reconstruct the sequence images and retrieve the targets. The experimental results reveal that the suggested model provides improved detection performance and higher robustness across various complicated scene types.

Infrared cirrus detection is extensively used in military and civil fields, but it poses several challenges to existing methods. These challenges include the complex and diverse shapes of the cirrus clouds, as well as their varying sizes. Additionally, false alarms can be easily triggered by the shadows of cirrus clouds and strong edges. Furthermore, dim and weak cirrus clouds blend into the background and lack distinct features, leading to missed detections. This paper presents an innovative infrared cirrus detection model based on multi-directional graph learning and local fractal feature prior weight mapping to overcome these challenges. Taking into account the structural characteristics of cirrus and background continuity, the proposed method utilizes graph learning enhanced matrix decomposition to separate the cirrus clouds and background. Additionally, to highlight cirrus clouds while suppressing strong edges in the background caused by mountains or rivers, weighted local fractal features are proposed as prior knowledge. To improve the detection of dim and small cirrus clouds as well as accelerate the convergence, a reweighting optimization scheme is proposed. The model is solved using the Alternating Direction Method of Multipliers (ADMM) framework. Extensive experiments demonstrate that the proposed scheme outperforms a variety of classic techniques in terms of detection performance.

Ship detection plays a crucial role in a variety of military and civilian marine inspection applications. Infrared images are irreplaceable data sources for ship detection due to their strong adaptability and excellent all-weather reconnaissance ability. However, previous researches mainly focus on visible light or synthetic aperture radar (SAR) ship detection, while infrared ship detection is left in a huge blind spot. The main obstacles to this dilemma lie in the absence of public datasets, small scale, and poor semantic information of infrared ships, and severe clutter in complex ocean environments. To address the above challenges, we propose a knowledge-driven context perception network (KCPNet) and construct a public dataset called infrared ship detection dataset (ISDD). In KCPNet, aiming at the small scale of infrared ships, a balanced feature fusion network (BFF-Net) is proposed to balance information from all backbone layers and generate nonlocal features with balanced receptive fields. Moreover, considering the key role of contextual information, a contextual attention network (CA-Net) is designed to improve robustness in complex scenes by enhancing target and contextual information and suppressing clutter. Inspired by prior knowledge of human cognitive processes, we construct a novel knowledge-driven prediction head to autonomously learn visual features and back-propagate the knowledge throughout the whole network, which can efficiently reduce false alarms. Extensive experiments demonstrate that the proposed KCPNet achieves state-of-the-art performance on ISDD. Source codes and ISDD are accessible athttps://github.com/yaqihan-9898.

To solve the problem of infrared (IR) small target tracking loss or error caused by factors such as scale changes, motion blur, occlusion, etc., this paper proposes a multi-strategy fusion tracking algorithm using an IR small target segmentation network as the detection head, which mainly includes six strategies: target pixel clustering, target feature threshold adjustment, large area search, small area tracking, gate tracking, and coordinate solution. First, candidate targets are obtained through the IR small target segmentation network and pixel clustering strategy. Second, the range of candidate targets is further reduced through threshold adjustment strategies. Then, real-time tracking of IR small targets is achieved through large area search, small area tracking, and wave gate tracking strategies. Finally, the longitude, latitude, and altitude of the tracked target are obtained through coordinate calculation strategies. Both qualitative and quantitative experiments based on real IR small target sequences verify that our algorithm can achieve more satisfactory performances in terms of success rate, precision, and robustness compared with other typical visual trackers. In addition, we have deployed tracking algorithms on the Orange Pi 5 embedded platform, and the tracking speed meets the real-time requirements.

Efficiently and accurately separating infrared (IR) small targets from complex backgrounds presents a significant challenge. Numerous studies in the literature have proposed various feature fusion modules designed specifically to enhance the extraction of IR small target features. While these designs offer some incremental improvement to the accuracy of IR small target detection, they come at a steep cost of significantly increasing network parameters and FLOPs. Striving for a balance between computational efficiency and model accuracy, we decided to forgo these complex feature fusion modules. Instead, we developed a new lightweight encoding and decoding structure known as the Lightweight IR Small Target Segmentation Network (LW-IRSTNet). This structure integrates regular convolutions, depthwise separable convolutions, atrous convolutions, and asymmetric convolutions modules. In addition, we devised post-processing modules including an eight-neighborhood clustering algorithm and an online target feature adjustment strategy. Experimental results indicate that: 1) the segmentation accuracy metrics of LW-IRSTNet match the best results of 14 state-of-the-art comparative baselines; 2) the parameters and FLOPs of LW-IRSTNet, at only 0.16M and 303M respectively, are significantly smaller in comparison to these baselines; and 3) the post-processing modules enhance both user-friendliness and the robustness of algorithm deployment. Moreover, LW-IRSTNet has been successfully implemented on both embedded platforms and websites, expanding its range of applications. Utilizing the ONNX framework, NPU acceleration, and CPU multi-threaded resource allocation, we have been able to achieve high-performance inference capabilities, as well as online dynamic threshold adjustment with the LW-IRSTNet. The source codes for this project can be accessed at https://github.com/kourenke/LW-IRSTNet.

Infrared small target detection (ISTD) is vital for civil and military applications. However, existing methods often face challenges in coping with complex scenes, discriminating targets from similar objects, or leveraging temporal information effectively. To tackle these limitations, we offer an innovative approach that exploits the spatio-temporal structure of infrared images. A four-dimensional (4D) infrared tensor is initially constructed from a sequence of infrared images, and decomposed into lower-dimensional tensors using the tensor train (TT) and its extension – tensor ring (TR) techniques. The ISTD problem is then formulated as a sparse plus low-rank decomposition problem, where the sparse part is the target and the low-rank part is the background. We factorize the composed tensors into matrices via TT and TR unfolding approaches, which mitigates the imbalance between different modes containing spatial and temporal information. By constraining the balanced unfolded components with the weighted sum of nuclear norm, we solve the problem using the alternating direction multiplier method (ADMM). Furthermore, we validate models on several datasets and benchmark them with state-of-the-art techniques in detection accuracy and background suppression. Comparison results demonstrate the superiority of our approach over the existing methods. Moreover, the results of an ablation study with three-dimensional (3D) tensor structures show the effectiveness and feasibility of the dimension expansion to 4D.

The research on uncertainty is a hot spot in artificial intelligence, especially in actual applications. Most research focus on measuring the uncertainty between evidence, ignoring the structure of evidence. When a sample has multiple features, the traditional structure of the focal element cannot handle and display the uncertainty between multiple features directly. Hence, we proposed a new structure of the focal element considering multiple features into one focal element and provided the corresponding basic probability assignment calculation. To apply the new structure in practices, we provided a new object recognition method combining the fractal feature and credible evidence. The new method can improve the robustness of the algorithm in a noisy environment. In the end, some experiments have illustrated the validity and correctness of the proposed method.

Infrared target detection is of great significance to various critical fields, however, it is still a crucial problem to robustly and quickly detect infrared small targets under complicated circumstances. In this letter, a novel detection method based on resampling-guided-image model is proposed. Initially, a new infrared image model based on guided image filtering is introduced as an improved supplant for normally used infrared patch-image (IPI) model. Our model approximates the effect of IPI model in strengthening the low-rankness of background components and provides a favorable presuppression to the noise components. Furthermore, the partial sum of singular values and nonnegativity are adopted to constrain the background image and the target image, respectively. Besides, the alternating direction method of multipliers is adopted to efficiently solve the model. A series of experiments show that the proposed algorithm has a more robust and efficient detection performance compared with other baselines.

Deep learning algorithms have recently provided new ideas for various change detection (CD) tasks, which have yielded promising results. However, accurately identifying urban land cover and land use (LCLU) changes remains challenging in the very high-resolution (HR) remote sensing images due to the difficulties in effectively modeling the features from ground objects with different times and different spatial locations. In this letter, a multiscale swin transformer (MST)-based deeply supervised network (MSTDSNet) is proposed for monitoring urban LCLU changes using bi-temporal very HR remote sensing images. The wider and deeper layer aggregation (WDLA) is first introduced to improve the distinguishability of multiscale features. Subsequently, MST is adopted to make the most of the available spatial information in the refined multiscale features. Moreover, channel-wise dependencies (CWDS) are integrated as a soft constraint to directly supervise WDLA. Experiments are conducted on the widely used SYSU-CD and LEVIR-CD datasets. Compared with other state-of-the-art CD methods, MSTDSNet provides favorable performance, with the highest F1 of 80.33% and 88.10%, respectively.

In this letter, we propose a new local feature-based method for the detection of infrared small targets with the density feature map that can effectively suppress noise in the feature domain. First, we combine the local tetra pattern (LTrP) and the second-order LTrP to generate the density feature map. Second, we apply density peaks searching to the feature map to obtain candidate targets. Third, we generate two local features, i.e., the entropy and third-order moment, for each image patch whose center is a candidate target and then fuse them to employ the fused feature to find the real target. The experimental results demonstrate that our proposed method achieves better performance compared with the state-of-the-art approaches.

Remote sensing ship recognition is widely used in civil and military applications, such as national defense construction, fishery management, and navigation supervision. Unfortunately, limited by the lack of public datasets for fine-grained ship recognition, current studies mainly focus on ship detection or coarse-grained ship recognition, whereas fine-grained recognition task is left out. The main challenges for fine-grained ship recognition include: 1) complex scenes and 2) ship characteristics of arbitrary orientation, dense distribution, and huge scale and appearance variation. Aiming at the challenges above, we propose a novel efficient information reuse network (EIRNet) and establish a public 20-class Dataset for Oriented Ship Recognition (DOSR). In our EIRNet, considering recognition robustness to multiscale ships, a dense feature fusion network (DFF-Net) with two fusion directions is designed to maximize the utilization of multilayer information and reduce information redundancy. Then, the fused feature maps are refined by a dual-mask attention module (DMAM) to improve performance in dense and clutter scenes by enhancing the distinction between ships and suppressing clutter. Furthermore, Mask-RPN improves the efficiency of generating proposals by reusing the attention mask. Finally, we introduce a concept of upper level class to mine interclass relationships, which further improves the recognition accuracy. Extensive experiments demonstrate that our EIRNet achieves state-of-the-art performance on DOSR and another popular public dataset called HRSC2016.

Infrared small target detection plays an important role in precision guidance, infrared warning, and other applications. The infrared patch-tensor (IPT) model has good detection performance, but some challenges still exist, such as the inaccurate representation of the background rank and poor robustness against noise and sparse interference. In order to solve these problems, a new IPT model is proposed in this article. First, to approximate the tensor rank more reasonably, t-SVD is generalized to multimodal t-SVD, and the tensor fibered rank is introduced. Moreover, the tensor fibered nuclear norm based on the Log operator (LogTFNN) is used to nonconvex approximate tensor fibered rank. Second, to suppress sparse interference such as strong edges and corner points, the prior information is extracted by the local structure tensor. Third, the hypertotal variation (HTV) is used as a joint regularization term to remove noise. Then, the alternating direction method of multipliers (ADMM) is used to solve the model. The proposed algorithm was tested on the 20 single-frame infrared images and six sequences of real scenes. Lots of experiments demonstrate that this algorithm has the robustness to noise and different scenes. Different evaluation metrics also show that the proposed algorithm has a significant superiority in detection performance compared with various state-of-the-art methods.

Infrared target detection technology is indispensable in precision guidance and infrared warning systems. It faces grant challenges under complex scenes, such as poor robustness against a variety of scenes, inaccurate separation of the target from similar goals, etc. To solve these problems, we present a novel small target detection method based on spatial–temporal information of infrared imageries. A nonoverlapping patch spatial–temporal tensor (NPSTT) model is established by sliding a window to obtain nonoverlapping patches in adjacent images. Moreover, the tensor capped nuclear norm (TCNN) is introduced, which approximates the tensor rank more accurately for local infrared images. TCNN regularization and NPSTT (TCNN-NPSTT) are adopted to detect the potential targets. Essentially, the procedure of extracting targets from the background is converted to the low rank and sparse tensor factorization. Besides, an efficient optimization scheme utilizing the alternating direction multiplier method (ADMM) is introduced to solve the proposed model. Experiments in various scenes show that NPSTT can obtain better performance against complex backgrounds than state-of-the-art baselines, using the evaluating indicators include the receiver operating characteristic curve (ROC), signal-to-clutter ratio gain (SCRG), and background suppression factor (BSF).

Robust online object tracking entails integrating short-term memory based trackers and long-term memory based trackers in an elegant framework to handle structural and appearance variations of unknown objects in an online manner. The integration and synergy between short-term and long-term memory based trackers have yet studied well in the literature, especially in pre-training free settings. To address this issue, this paper presents a bottom-up and top-down integration framework. The bottom-up component realizes a data-driven approach for particle generation. It exploits a short-term memory based tracker to generate bounding box proposals in a new frame. In the top-down component, this paper presents a graph regularized sparse coding scheme as the long-term memory based tracker. The over-complete bases for sparse coding are composed of part-based representations learned from earlier tracking results and new observations to form a space with rich temporal context information. A particle graph is computed whose nodes are the bottom-up discriminative particles and edges are formed on-the-fly in terms of appearance and spatial-temporal similarities between particles. The particle graph induces a regularization term in optimizing the sparse coding coefficients for bottom-up particles. In experiments, the proposed method is tested on the widely used OTB-100 benchmark and the VOT2016 benchmark with better performance obtained than baselines including deep learning based trackers. In addition, the outputs from the top-down sparse coding are potentially useful for downstream tasks such as action recognition, multiple-object tracking, and object re-identification.

In this work, a novel algorithm for detecting and separating cloud from single image is proposed. This approach utilizes random fractal model and histogram intersection to select the appropriate atoms from the image itself for dictionary learning and sparse representation which avoids difficult requirement of collecting a set of images and the corresponding labels for classification-based method or pre-learned dictionary based sparse representation method. Thus, the proposed method reduces the requirement of input data and realizes automatic cloud detection only from one image. Experiments conducted on NWPU-RESISC45 dataset indicate that the proposed method can detect cloud effectively and achieve competitive performance compared with other approaches.

Robust small-target detection plays an important role in the infrared (IR) search and track system, but it is still a challenge to detect small IR target under complex background. In this letter, an effective method inspired by the scale-space theory and the contrast mechanism of the human vision system is proposed. First, Gaussian scale-space (GSS) of an IR image is constructed by the convolution of a variable-scale Gaussian function. Second, the gray features of the local image can be directly represented by downsampling in a scale image, and enhanced local contrast measure (ELCM) is defined to enhance small target and suppress complex background. Then, the saliency map is obtained by using max-pooling operation, and an adaptive threshold is adapted to segment real targets. Experimental results on a test set with three real IR sequences demonstrate that the proposed method has a good performance in target enhancement and background suppression, and shows strong robustness under complex background. Especially, the proposed method has high computational efficiency, which can improve detection speed.

Robust detection of infrared small target is still a challenge due to the diversity and complexity of the background. In this letter, we propose a novel detection approach based on density peaks searching and maximum-gray region growing. The main idea is that infrared small targets can be described by three features: a relatively high density, a relatively large distance from pixels with higher density, and a relatively large density gap between targets and their neighbors. This idea helps to establish a detection procedure which can detect small targets of different sizes and remove the interference caused by clutters of various complex shapes. A quartile-based technique is introduced to obtain a more robust decision threshold for multiple scenes. Compared with eight state-of-the-art algorithms, the proposed method shows a superior detection performance and an acceptable efficiency in extensive experiments.

Infrared small target detection is one of the key techniques in the infrared search and track system. Frequency differences among target, background, and noise are often important information for target detection. In this letter, a nonnegativity-constrained variational mode decomposition (NVMD) method is proposed. Unlike the traditional frequency-domain methods, the proposed method can adaptively decompose the input signal into several separated band-limited subsignals, with the nonnegativity constraint. First, a bandpass filter is used as a preprocessing step. Second, by exploring the frequency and nonnegativity properties of the small target, the NVMD model is constructed. The potential target subsignal can be obtained by solving the NVMD model. By performing threshold segmentation on the potential target subsignal, we can obtain the detection result of the infrared small target. Experiments on six real infrared image sequences demonstrate that the proposed method has a good performance in target enhancement and background suppression. Additionally, the proposed method shows strong robustness under various backgrounds.

Infrared (IR) dim and small target detection in a highly complex background play an important role in many applications, and remain a challenging problem. In this paper, a novel method named stable multisubspace learning is presented to deal with this problem. The new method takes into account the inner structure of actual images so that it overcomes the shortage of the traditional method. First, by analyzing the multisubspace structure of heterogeneous background data, a corresponding image model is proposed using subspace learning strategy. This model is also stable to noise interference. Second, an efficient optimization algorithm is designed to solve the proposed IR image model. By adding the proper postprocessing procedure, we can get the detection result. Experiments on simulation scenes and real scenes show that the proposed method has superior detection ability under heterogeneous background.

This paper proposes a novel covariation approach for seismic wavelet estimation under the assumption that a real seismic signal follows non-Gaussian α-stable distributions. Since the non-Gaussian α-stable signals do not have finite second or higher order moments, the traditional methods of Gaussian distribution may not get a suitable solution. Based on the principle of fractional lower order statistics, the covariation approach deconvolution objective function matrix was given, and the details of wavelet estimation with the covariation approach were presented. Furthermore, computer simulation experiments on theoretical synthetic data and real seismic data were conducted. In the experiments, the effect of moments was considered. Among the estimated wavelets with different moments, the best wavelet should be the one with moment less than characteristic but close to characteristic. To verify the correctness and effectiveness of the proposed method, the extracted real wavelet was applied in real seismic acoustic impedance inversion. The result from the inversion of the 2-D real data set is consistent with the well log interpretation very well.