In view of the issues of missed and false detections encountered in small object detection for UAV remote sensing images, and the inadequacy of existing algorithms in terms of complexity and generalization ability, we propose a small object detection model named IA-YOLOv8 in this paper. This model integrates the intra-group multi-scale fusion attention mechanism and the adaptive weighted feature fusion approach. In the feature extraction phase, the model employs a hybrid pooling strategy that combines Avg and Max pooling to replace the single Max pooling operation used in the original SPPF framework. Such modifications enhance the model’s ability to capture the minute features of small objects. In addition, an adaptive feature fusion module is introduced, which is capable of automatically adjusting the weights based on the significance and contribution of features at different scales to improve the detection sensitivity for small objects. Simultaneously, a lightweight intra-group multi-scale fusion attention module is implemented, which aims to effectively mitigate background interference and enhance the saliency of small objects. Experimental results indicate that the proposed IA-YOLOv8 model has a parameter quantity of 10.9 MB, attaining an average precision (mAP) value of 42.1% on the Visdrone2019 test set, an mAP value of 82.3% on the DIOR test set, and an mAP value of 39.8% on the AI-TOD test set. All these results outperform the existing detection algorithms, demonstrating the superior performance of the IA-YOLOv8 model in the task of small object detection for UAV remote sensing.

Spaceborne synthetic aperture radar (SAR) image ship detection is an important tool to ensure the safety of sea areas and improve the efficiency of maritime traffic. Due to the sparse distribution of ships in the vast ocean, many imaging results are redundant. Furthermore, SAR imaging consumes huge computing, storage, and communication resources. The range-compressed data, without azimuth compression calculation, has caught our attention. Nevertheless, the echo energy of the ship is scattered in the azimuth direction, making it difficult to detect. Several deep learning-based methods are proposed, yet their performance is constrained by labeled datasets. As they neglect sea clutter interference, these methods are also impractical. To address these issues, this paper proposes a constant false alarm rate (CFAR)-based ship detector for range-compressed SAR data. Firstly, the imaging process of spaceborne SAR signal is reviewed and analyzed. Then, a generalized Gamma distribution based sea clutter model is proposed for the SAR range focused domain. Next, a CFAR-based method for detecting ships in range-compressed SAR data is customized. Finally, experiments are conducted on Sentinel-1 and ERS-2 SAR data. The results show that the proposed sea clutter model has high goodness-of-fit, and the customized CFAR-based method effectively detects ship targets. In summary, ship detection in range-compressed SAR data is very promising research.

Small target detection in Unmanned Aerial Vehicle (UAV) remote sensing images faces challenges such as insufficient feature information and background interference in complex environments. Existing single-modal methods, which rely solely on one data source, struggle to address these issues effectively. This paper proposes CA-YOLO, a lightweight detection framework based on adaptive cross-modal feature fusion and an attention mechanism. The framework employs a dual-branch backbone network to extract complementary features from RGB and IR modalities, utilizes an Adaptive Cross-modal Feature Fusion (ACFF) module to dynamically integrate global semantics and local details, and leverages a Lightweight Hybrid Attention (LHA) mechanism to enhance targets and suppress noise. Experiments on three public datasets, DroneVehicle, LLVIP, and FLIR, demonstrate that CA-YOLO achieves mAPs of 75.1%, 96.1%, and 71.4%, respectively, while requiring only 6.8 MB parameters and 16.4 GFLOPs computational cost, significantly outperforming state-of-the-art methods. The inference speeds reach 57.1 FPS, 78.1 FPS, and 56.5 FPS on the respective datasets, achieving an optimal balance between accuracy and efficiency. Ablation studies and visualization results further validate the effectiveness of each module and the framework's superior capability in reducing missed detection and false alarm rates.

Recently, hyperspectral image (HSI) mixed denoising methods based on nonlocal subspace representation (NSR) have achieved significant success. However, most of these methods focus on optimizing the denoiser for representation coefficient images (RCIs) without considering how to construct RCIs that better inherit the spatial structure of the clean HSI, thereby affecting subsequent denoising performance. Although existing works have constructed RCIs from the perspective of sparse principal component analysis (SPCA), the refinement of RCIs in mixed noise conditions still leaves much to be desired. To address the aforementioned challenges, in this paper, we reconstructed robust RCIs based on SPCA in mixed noise circumstances to better preserve the spatial structure of the clean HSI. Furthermore, we propose to utilize the robust RCIs as prior information and perform iterative denoising in the denoiser that incorporates low-rank approximation. Extensive experiments conducted on both simulated and real HSI datasets demonstrate that the proposed robust RCIs guidance and low-rank approximation method, denoted as RRGNLA, exhibits competitive performance in terms of mixed denoising accuracy and computational efficiency. For instance, on the Washington DC Mall (WDC) dataset in Case 3, the denoising quantitative metrics of the mean peak signal-to-noise ratio (MPSNR), mean structural similarity index (MSSIM), and spectral angle mean (SAM) are 36.06 dB, 0.963, and 3.449, respectively, with a running time of 35.24 s. On the Pavia University (PaU) dataset in Case 4, the denoising quantitative metrics of MPSNR, MSSIM, and SAM are 34.34 dB, 0.924, and 5.505, respectively, with a running time of 32.79 s.