Based on the versatility and effectiveness of the siamese neural network, the technology of unmanned aerial vehicle visual object tracking has found widespread application in various fields including military reconnaissance, intelligent transportation, and visual positioning. However, due to complex factors, such as occlusions, viewpoint changes, and interference from similar objects during UAV tracking, most existing siamese neural network trackers struggle to combine superior performance with efficiency. To tackle this challenge, this paper proposes a novel SiamSTM tracker that is based on Slight Aware Enhancement Transformer and Multiple matching networks for real-time UAV tracking. The SiamSTM leverages lightweight transformers to encode robust target appearance features while using the Multiple matching networks to fully perceive response map information and enhance the tracker’s ability to distinguish between the target and background. The results are impressive: evaluation results based on three UAV tracking benchmarks showed superior speed and precision. Moreover, SiamSTM achieves over 35 FPS on NVIDIA Jetson AGX Xavier, which satisfies the real-time requirements in engineering.

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.

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.

UAV visual-object-tracking technology based on Siamese neural networks has great scientific research and practical application value, and it is widely used in geological surveying, reconnaissance monitoring, and environmental monitoring. Due to the limited onboard computational resources and complex real-world environments of drones, most of the existing tracking systems based on Siamese neural networks struggle to combine excellent performance with high efficiency. Therefore, the key issue is to study how to improve the accuracy of target tracking under the challenges of real-time performance and the above factors. In response to this problem, this paper proposes a real-time UAV tracking system based on interframe saliency transformer and lightweight multidimensional attention network (SiamITL). Specifically, interframe saliency transformer is used to continuously perceive spatial and temporal information, making the network more closely related to the essence of the tracking task. Additionally, a lightweight multidimensional attention network is used to better capture changes in both target appearance and background information, improving the ability of the tracker to distinguish between the target and background. SiamITL is effective and efficient: extensive comparative experiments and ablation experiments have been conducted on multiple aerial tracking benchmarks, demonstrating that our algorithm can achieve more robust feature representation and more accurate target state estimation. Among them, SiamITL achieved success and accuracy rates of 0.625 and 0.818 in the UAV123 benchmark, respectively, demonstrating a certain level of leadership in this field. Furthermore, SiamITL demonstrates the potential for real-time operation on the embedded platform Xavier, highlighting its potential for practical application in real-world scenarios.

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.

Due to the uniqueness of its perspective and the continuity of tracking, the current utilization of UAV in tracking has demonstrated immense potential in the field of aviation remote sensing. However, constrained by the complexities of the real-world environment during the tracking process and the limited computational capabilities of the onboard computing platform, existing tracking networks struggle to effectively integrate superior tracking performance with efficient computational speed. Addressing this pivotal issue, we proposed the Separation Fusion Transformer and Efficient Reuse Matching Network for Aerial tracking (SFTrack). Specifically, we designed the Separation Fusion Transformer, leveraging a meticulously crafted low-latency Transformer architecture to extract robust feature information of the target, thereby enhancing the algorithm’s ability to distinguish the target from intricate backgrounds. Furthermore, the Efficient Reuse Matching Network efficiently performs feature matching to accurately determine the target’s scale and location. SFTrack exhibits efficient and precise performance in drone tracking tasks. When compared to other drone-to-ground tracking algorithms, SFTrack demonstrates a significant lead in accuracy across multiple datasets, achieving an impressive 54 frames persecond (FPS) on embedded platforms. This validates the feasibility and effectiveness of our tracker in practical drone deployments.