As an atmospheric phenomenon, haze significantly reduces the visibility of outdoor and remote sensing images. Remote sensing and outdoor imaging have different mechanisms, existing dehazing methods are hard to be applied for both outdoor images and remote sensing images. In this paper, an efficient dehazing method is proposed which can be applied for both outdoor and remote sensing images. Dehazing methods based on image enhancement and image restoration methods are both advantages of the proposed method. To address the problem of inaccurate calculation of transmittance in existing methods, a Gaussian-weighted image fusion is introduced to obtain a refined transmittance. In addition, to solve the problem of image colour distortion after dehazing, an unsharp mask method is used to correct the dehazed image. Experiments on the synthetic dataset and real dataset show that the proposed method is able to dehaze both the outdoor and remote sensing images, and outperforms existing methods by visual inspection. On the RICE remote sensing image dataset, the proposed method has an image peak signal-to-noise ratio of 27.08 and structural similarity of 0.94, which are higher than other methods.

Object detection in UAV images is an important and challenging task for many applications, which often needs highly efficient detection algorithms to meet the accuracy and real-time requirements of the applications. In this paper, we investigate efficient mechanisms for detecting dense and small objects in UAV images. Specifically, 1) kernel K-means is used to obtain optimal anchors for dense and small object detection; 2) a spatial information enhancement module (SIE) is proposed to improve the detection accuracy of dense objects by extracting object spatial location information; 3) a Coord_C3 module is proposed to improve the receptive field of the network and to reduce the number of network parameters; 4) a small detection head is added in the Head of network and skip connections are employed in the Neck of network to improve the detection accuracy of small objects. Experimental results on the VisDrone2019, LEVIR-ship and Stanford Drone datasets show that our method not only has higher detection accuracy, but also runs faster compared to state-of-the-art detection methods.

Ship detection is of great importance in territorial security and marine environmental protection. However, the accurate detection of small ships is a challenging task in complex environments mainly due to small ships having few features on remote sensing images. In this letter, we propose a small ship detection model based on YOLOv5s. The major features of the proposed model include: (1) a detection layer is added with a shallow feature map in the Head and skip connections are employed in the Neck to improve the detection accuracy of small ships; (2) a novel and effective Hybrid Spatial Pyramid Pooling (HSPP) is proposed to fuse the local and global information of feature maps; (3) a Coordinate Attention mechanism is employed in the Backbone to augment the representations of small ships, and EIOU is used as the loss function for bounding box regression to enhance the localization accuracy of the proposed model; (4) K-means++ algorithm is used to obtain more reasonable anchors for small ship detection. We introduce the multi-scale ship dataset OSSD, which contains 10133 images. Experiments on LEVIR-Ship and OSSD validate the effectiveness of our proposed model. The code and OSSD will be available at https://github.com/wenjieo/Improved-YOLOv5s-for-small-ship-detection.

Non-homogeneous haze in UAV-based remote sensing images severely deteriorates image quality, introducing significant challenges for downstream interpretation and analysis tasks. To tackle this issue, we propose UAVD-Net, a novel dehazing framework specifically designed to enhance UAV remote sensing imagery affected by spatially varying haze. UAVD-Net integrates both global and local feature extraction mechanisms to effectively remove non-uniform haze across different spatial regions. A Transformer-based Multi-layer Global Information Capturing (MGIC) module is introduced to progressively capture and integrate global contextual features across multiple layers, enabling the model to perceive and adapt to spatial variations in haze distribution. This design significantly enhances the network’s ability to model large-scale structures and correct non-homogeneous haze across the image. In parallel, a local information extraction sub-network equipped with an Adaptive Local Information Enhancement (ALIE) module is used to refine texture and edge details. Additionally, a Cross-channel Feature Fusion (CFF) module is incorporated in the decoder stage to effectively merge global and local features through a channel-wise attention mechanism, generating dehazed outputs that are both structurally coherent and visually natural. Extensive experiments on synthetic and real-world datasets demonstrate that UAVD-Net consistently outperforms existing state-of-the-art dehazing methods.

To improve the accuracy and robustness of anti-UAV detection, this study introduces TransAUAV, a Transformer-based RGB- infrared image fusion detection network. This approach strengthens object feature representation by leveraging multi-modal data fusion and self-attention mechanisms. Specifically, 1) we propose a multi-modal attention fusion module, which enhances the complementarity of RGB and infrared image features through a dual-path attention mechanism; 2) we propose a cross-layer multi-scale Transformer module, which improves detection performance by extracting multi-scale features and facilitating cross-modal information interaction; 3) we propose a texture information focus module, which enhances the representation of local texture details. Additionally, this paper designs a hybrid loss function to improve feature discrimination capability and training efficiency. Experimental results on anti-UAV and Drone-detection datasets show that TransAUAV outperforms state-of-the-art methods on all evaluation metrics.