Due to rich spectral information, hyperspectral images (HSIs) have been widely used in various fields. However, limited by imaging systems, the low spatial resolution of HSIs has become an important problem. In this article, for enhancing the spatial resolution, Interactformer is proposed to interact with global and local features extracted by transformer and 3-D convolutional neural network (CNN) branches. Within the transformer branch, a separable self-attention module with linear complexity is designed to solve the problem that traditional self-attention mechanisms suffer from large memory costs due to quadratic complexity. In the 3-D CNN branch, the spectral attention module and 3-D convolution are applied jointly to better protect the spectral correlation among spectral bands and facilitate local feature extraction of HSIs. The interactive attention unit between the two parallel branches is designed to interact with local and global feature information adaptively. Compared with state-of-the-art super-resolution (SR) methods, the proposed method reconstructs better HSI in simulated SR experiments, real SR experiments, and classification experiments, which proves that Interactformer can effectively improve the spatial resolution while preserving the spectral information.

Coastal wetland monitoring plays an important role in the protection and restoration of ecosystems in this world. UAV-hyperspectral imaging, as an emerging technique for Earth observation and space exploration, provides the huge potential ability to identify different wetland species. In this work, a multilayer global spectral–spatial attention network (MGSSAN) is proposed for mapping coastal wetlands, which mainly consists of two major steps. First, a two-branch convolutional neural network (CNN) framework with residual connection is developed to obtain an initial classification probability map, in which one branch is used to capture the spectral information, the other branch is used to extract spatial information, and a global spectral–spatial attention module is designed to guide networks focusing on those features that are more discriminative. Second, an extended random walker method is utilized to optimize the initial classification probabilities, so as to yield the final map. Experiments performed on three wetland HSI datasets created by ourselves verify that the proposed method can obtain superior performance with respect to several state-of-the-art hyperspectral image classification methods.

Pan-sharpening is an effective method to obtain high-resolution multispectral images by fusing panchromatic (PAN) images with fine spatial structure and low-resolution multispectral images with rich spectral information. In this article, a multiscale pan-sharpening method based on dynamic convolutional neural network is proposed. The filters in dynamic convolution are generated dynamically and locally by the filter generation network which is different from the standard convolution and strengthens the adaptivity of the network. The dynamic filters are adaptively changed according to the input images. The proposed multiscale dynamic convolutions extract detail feature of PAN image at different scales. Multiscale network structure is beneficial to obtain effective detail features. The weights obtained by the weight generation network are used to adjust the relationship among the detail features in each scale. The GeoEye-1, QuickBird, and WorldView-3 data are used to evaluate the performance of the proposed method. Compared with the widely used state-of-the-art pan-sharpening approaches, the experimental results demonstrate the superiority of the proposed method in terms of both objective quality indexes and visual performance.

Power line parts detection refers to the inspection of key parts on transmission lines against the complex background in aerial images and identifying whether exist anomalies that cause transmission failure. Obviously, this process plays a pivotal role in ensuring the safety of power transmission. Most of the existing methods are based on deep convolutional neural networks. However, the complexity and variability of the aerial image background and the problem of unmanned aerial vehicles (UAVs) shooting perspective and distance pose a challenge for previous works. This study aims to improve the detection accuracy of the model and propose an attention-guided multitask convolutional neural network (AGMNet). First, to enhance the feature representation of objects in aerial images, we construct spatial region attention blocks that are suitable for object detection. It can be inserted into any existing convolutional backbone network. Due to its efficient feature tensor computation method, the network can obtain competitive results with less computational memory. Second, we introduce a multitask framework that creatively considers the identification of rust levels and abnormal conditions of power line components, which has not been considered in previous works. Finally, we incorporate the refinable region proposal network (RPN) structure and multiscale training strategy to improve the robustness of the network. The experimental results on the testing datasets show that the proposed AGMNet can recognize the power parts (dampers and suspension clamps) with a mean average precision (mAP) of 95.3% and simultaneously identify their rust levels with an mAP of 75.4% and abnormal conditions with an mAP of 92.7%.

Crafting an edge-based real-time object detector for unmanned aerial vehicle (UAV) aerial images is challenging because of the limited computational resources and the small size of detected objects. Existing lightweight object detectors often prioritize speed over detecting extremely small targets. To better balance this trade-off, this paper proposes an efficient and low-complexity object detector for edge computing platforms deployed on UAVs, termed EUAVDet (Edge-based UAV Object Detector). Specifically, an efficient feature downsampling module and a novel multi-kernel aggregation block are first introduced into the backbone network to retain more feature details and capture richer spatial information. Subsequently, an improved feature pyramid network with a faster ghost module is incorporated into the neck network to fuse multi-scale features with fewer parameters. Experimental evaluations on the VisDrone, SeaDronesSeeV2, and UAVDT datasets demonstrate the effectiveness and plug-and-play capability of our proposed modules. Compared with the state-of-the-art YOLOv8 detector, the proposed EUAVDet achieves better performance in nearly all the metrics, including parameters, FLOPs, mAP, and FPS. The smallest version of EUAVDet (EUAVDet-n) contains only 1.34 M parameters and achieves over 20 fps on the Jetson Nano. Our algorithm strikes a better balance between detection accuracy and inference speed, making it suitable for edge-based UAV applications.