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The Unmanned Aerial Vehicle (UAV) tracking tasks find extensive utility across various applications. However, current Transformer-based trackers are generally tailored for diverse scenarios and lack specific designs for UAV applications. Moreover, due to the complexity of training in tracking tasks, existing models strive to improve tracking performance within limited scales, making it challenging to directly apply lightweight designs. To address these challenges, we introduce an efficient attention mechanism known as Macaron Attention, which we integrate into the existing UAV tracking framework to enhance the model’s discriminative ability within these constraints. Specifically, our attention mechanism comprises three components, fixed window attention (FWA), local squeezing global attention (LSGA), and conventional global attention (CGA), collectively forming a Macaron-style attention implementation. Firstly, the FWA module addresses the multi-scale issue in UAVs by cropping tokens within a fixed window scale in the spatial domain. Secondly, in LSGA, to adapt to the scale variation, we employ an adaptive clustering-based token aggregation strategy and design a “window-to-window” fusion attention model to integrate global attention with local attention. Finally, the CGA module is applied to prevent matrix rank collapse and improve tracking performance. By using the FWA, LSGA, and CGA modules, we propose a brand-new tracking model named MATrack. The UAV123 benchmark is the major evaluation dataset of MATrack with 0.710 and 0.911 on success and precision, individually.

The detection of an infrared small target faces the problems of background interference and non-obvious target features, which have yet to be efficiently solved. By employing the non-local self-correlation characteristic of the infrared images, the principle component pursuit (PCP)-based methods are demonstrated to be applicable to infrared small target detection in a complex scene. However, existing PCP-based methods heavily depend on the uniform distribution of the background pixels and are prone to generating a high number of false alarms under strong clutter situations. In this paper, we propose a group low-rank regularized principle component pursuit model (GPCP) to solve this problem. First, the local image patches are clustered into several groups that correspond to different grayscale distributions. These patch groups are regularized with a group low-rank constraint, enabling an independent recovery of different background regions. Then, GPCP model integrates the group low-rank components with a global sparse component to extract small targets from the background. Different singular value thresholds can be exploited for image groups corresponding to different brightness and grayscale variance, boosting the recovery of background clutters and also enhancing the detection of small targets. Finally, a customized optimization approach based on alternating direction method of multipliers is proposed to solve this model. We set three representative detection scenes, including the ground background, sea background and sky background for experiment analysis and model comparison. The evaluation results show the proposed model has superiority in background suppression and achieves better adaptability for different scenes compared with various state-of-the-art methods.

Due to their devastating ability to extract features, convolutional neural network (CNN)-based approaches have achieved tremendous success in hyperspectral image (HSI) classification. However, previous works have been dedicated to constructing deeper or wider deep learning networks to obtain exceptional classification performance, but as the layers get deeper, the gradient disappearance problem impedes the convergence stability of network models. Additionally, previous works usually focused on utilizing fixed-scale convolutional kernels or multiple available, receptive fields with varying scales to capture features, which leads to the underutilization of information and is vulnerable to feature learning. To remedy the above issues, we propose an innovative hybrid-scale feature enhancement network (HFENet) for HSI classification. Specifically, HFENet contains two key modules: a hybrid-scale feature extraction block (HFEB) and a shuffle attention enhancement block (SAEB). HFEB is designed to excavate spectral–spatial structure information of distinct scales, types, and branches, which can augment the multiplicity of spectral–spatial features while modeling the global long-range dependencies of spectral–spatial informative features. SAEB is devised to adaptively recalibrate spectral-wise and spatial-wise feature responses to generate the purified spectral–spatial information, which effectively filters redundant information and noisy pixels and is conducive to enhancing classification performance. Compared with several sophisticated baselines, a series of experiments conducted on three public hyperspectral datasets showed that the accuracies of OA, AA, and Kappa all exceed 99%, demonstrating that the presented HFENet achieves state-of-the-art performance.

Multiple object tracking (MOT) in videos captured by unmanned aerial vehicle (UAV) is a fundamental aspect of computer vision. Recently, the one-shot tracking paradigm integrates the detection and re-identification (ReID) tasks, striking a balance between tracking accuracy and inference speed. This paradigm alleviates task conflicts and achieves remarkable results through various feature decoupling methods. However, in challenging scenarios like drone movements, lighting changes and object occlusion, it still encounters issues with detection failures and identity switches. In addition, traditional feature decoupling methods directly employ channel-based attention to decompose the detection and ReID branches, without a meticulous consideration of the specific requirements of each branch. To address the above problems, we introduce an asymmetric feature enhancement network with a global coordinate-aware enhancement (GCAE) module and an embedding feature aggregation (EFA) module, aiming to optimize the two branches independently. On the one hand, we develop the GCAE module for the detection branch, which effectively merges rich semantic information within the feature space to improve detection accuracy. On the other hand, we introduce the EFA module for the ReID branch, which highlights the significance of pixel-level features and acquires discriminative identity embedding through a local feature aggregation strategy. By efficiently incorporating the GCAE and EFA modules into the one-shot tracking pipeline, we present a novel MOT framework, named AsyUAV. Extensive experiments have demonstrated the effectiveness of our proposed AsyUAV. In particular, it achieves a MOTA of 38.3% and IDF1 of 51.7% on VisDrone2019, and a MOTA of 48.0% and IDF1 of 67.5% on UAVDT, outperforming existing state-of-the-art trackers.

Convolutional neural networks (CNNs) have shown outstanding feature extraction capability and become a hot topic in the field of hyperspectral image (HSI) classification. However, most of the prior works usually focus on designing deeper or wider network architectures to extract spatial and spectral features, which give rise to difficulty for optimization and more parameters along with higher computation. Moreover, how to learn spatial and spectral information more effectively is still being researched. To tackle the aforementioned problems, a decompressed spectral-spatial multiscale semantic feature network (DSMSFNet) for HSI classification is proposed. This model is composed of a decompressed spectral-spatial feature extraction module (DSFEM) and a multiscale semantic feature extraction module (MSFEM). The former is devised to extract more discriminative and representative global decompressed spectral-spatial features in a lightweight extraction manner, while the latter is constructed to expand the range of available receptive fields and generate clean multiscale semantic features at a granular level to further enhance the classification performance. Compared with progressive classification approaches, abundant experimental results on three benchmark datasets prove the superiority of our developed DSMSFNet model.

A hyperspectral image (HSI) has a very high spectral resolution, which can reflect the target’s material properties well. However, the limited spatial resolution poses a constraint on its applicability. In recent years, some hyperspectral pansharpening studies have attempted to integrate HSI with PAN to improve the spatial resolution of HSI. Although some achievements have been made, there are still shortcomings, such as insufficient utilization of multi-scale spatial and spectral information, high computational complexity, and long network model inference time. To address the above issues, we propose a novel U-shaped hyperspectral pansharpening network with channel cross-concatenation and spatial–spectral attention mechanism (CCC-SSA-UNet). A novel channel cross-concatenation (CCC) method was designed to effectively enhance the fusion ability of different input source images and the fusion ability between feature maps at different levels. Regarding network design, integrating a UNet based on an encoder–decoder architecture with a spatial–spectral attention network (SSA-Net) based on residual spatial–spectral attention (Res-SSA) blocks further enhances the ability to extract spatial and spectral features. The experiment shows that our proposed CCC-SSA-UNet exhibits state-of-the-art performance and has a shorter inference runtime and lower GPU memory consumption than most of the existing hyperspectral pansharpening methods.

Convolutional neural networks (CNNs) have demonstrated impressive performance and have been broadly applied in hyperspectral image (HSI) classification. However, two challenging problems still exist: the first challenge is that redundant information is averse to feature learning, which damages the classification performance; the second challenge is that most of the existing classification methods only focus on single-scale feature extraction, resulting in underutilization of information. To resolve the two preceding issues, this article proposes a multiscale cross interaction attention network (MCIANet) for HSI classification. First, an interaction attention module (IAM) is designed to highlight the distinguishability of HSI and dispel redundant information. Then, a multiscale cross feature extraction module (MCFEM) is constructed to detect spectral–spatial features at different scales, convolutional layers, and branches, which can increase the diversity of spectral–spatial features. Finally, we introduce global average pooling to compress multiscale spectral–spatial features and utilize two fully connection layers, two dropout layers to obtain the output classification results. Massive experiments on three benchmark datasets demonstrate the superiority of our presented method compared with the state-of-the-art methods.

Recently, hyperspectral image (HSI) classification methods based on convolutional neural networks (CNN) have shown impressive performance. However, HSI classification still faces two challenging problems: the first challenge is that most existing classification approaches only focus on exploiting the fixed-scale convolutional kernels to extract spectral–spatial features, which leads to underutilization of information; the second challenge is that HSI contains a large amount of redundant information and noise, to a certain extent, which influences the classification performance of CNN. In order to tackle the above problems, this article proposes a multibranch crossover feature attention network (MCFANet) for HSI classification. The MCFANet involves two primary submodules: a cross feature extraction module (CFEM) and rearranged attention module (RAM). The former is devised to capture joint spectral–spatial features at different convolutional layers, scales and branches, which can increase the diversity and complementarity of spectral–spatial features, while the latter is constructed to spontaneously concentrate on recalibrating spatial-wise and spectral-wise feature responses, meanwhile exploit the shifted cascade operation to rearrange the obtained attention-enhanced features to dispel redundant information and noise, and thus, boost the classification performance. Compared with the state-of-the-art classification methods, massive experiments on four benchmark datasets demonstrate the meliority of our presented method.

Convolutional neural networks are widely applied in hyperspectral image (HSI) classification and show excellent performance. However, there are two challenges: the first is that fine features are generally lost in the process of depth transfer; the second is that most existing studies usually restore to first-order features, whereas they rarely consider second-order representations. To tackle the above two problems, this article proposes a hybrid-order spectral-spatial feature network (HS2FNet) for hyperspectral image classification. This framework consists of a precedent feature extraction module (PFEM) and a feature rethinking module (FRM). The former is constructed to capture multiscale spectral-spatial features and focus on adaptively recalibrate channel-wise and spatial-wise feature responses to achieve first-order spectral-spatial feature distillation. The latter is devised to heighten the representative ability of HSI by capturing the importance of feature cross-dimension, while learning more discriminative representations by exploiting the second-order statistics of HSI, thereby improving the classification performance. Massive experiments demonstrate that the proposed network achieves plausible results compared with the state-of-the-art classification methods.

Due to end-to-end optimization characteristics and fine generalization ability, convolutional neural networks have been widely applied to hyperspectral image (HSI) classification, playing an irreplaceable role. However, previous studies struggle with two major challenges: (1) HSI contains complex topographic features, the number of labeled samples in different categories is unbalanced, resulting in poor classification for categories with few labeled samples; (2) With the deepening of neural network models, it is difficult to extract more discriminative spectral-spatial features. To address the issues mentioned above, we propose a discriminative spectral-spatial-semantic feature network based on shuffle and frequency attention mechanisms for HSI classification. There are four main parts of our approach: spectral-spatial shuffle attention module (SSAM), context-aware high-level spectral-spatial feature extraction module (CHSFEM), spectral-spatial frequency attention module (SFAM), and cross-connected semantic feature extraction module (CSFEM). First, to fully excavate the category attribute information, SSAM based on a “Deconstruction-Reconstruction” structure is designed, solving the problem of poor classification performance caused by an unbalanced number of label samples. Considering that deep spectral-spatial features are difficult to extract, CHSFEM and SFAM are constructed. The former is based on the “Horizontal-Vertical” structure to capture context-aware high-level multiscale features. The latter introduces multiple frequency components to compress channels to obtain more multifarious features. Finally, towards suppressing noisy boundaries efficiently and capturing abundant semantic information, CSFEM is devised. Numerous experiments are implemented on four public datasets: the evaluation indexes of OA, AA and Kappa on four datasets all exceed 99%, demonstrating that our method can achieve satisfactory performance and is superior to other contrasting methods.

Multifarious hyperspectral image (HSI) classification methods based on convolutional neural networks (CNN) have been gradually proposed and achieve a promising classification performance. However, hyperspectral image classification still suffers from various challenges, including abundant redundant information, insufficient spectral-spatial representation, irregular class distribution, and so forth. To address these issues, we propose a novel 2D-3D CNN with spectral-spatial multi-scale feature fusion for hyperspectral image classification, which consists of two feature extraction streams, a feature fusion module as well as a classification scheme. First, we employ two diverse backbone modules for feature representation, that is, the spectral feature and the spatial feature extraction streams. The former utilizes a hierarchical feature extraction module to capture multi-scale spectral features, while the latter extracts multi-stage spatial features by introducing a multi-level fusion structure. With these network units, the category attribute information of HSI can be fully excavated. Then, to output more complete and robust information for classification, a multi-scale spectral-spatial-semantic feature fusion module is presented based on a Decomposition-Reconstruction structure. Last of all, we innovate a classification scheme to lift the classification accuracy. Experimental results on three public datasets demonstrate that the proposed method outperforms the state-of-the-art methods.