Recently, the U-Shaped networks has been widely explored in remote sensing image dehazing and obtained promising performance. However, most of the existing dehazing methods based on U-Shaped framework lack the reconstruction constraints of haze areas, which is particularly important to restore haze-free images. Moreover, their encoding and decoding layers cannot effectively fuse multi-scale features, resulting in deviations in the color and texture of the dehazing image. To address these issues, in this paper, we propose a Partial Siamese with Multiscale Bi-codec Dehazing Network (PSMB-Net) which is mainly composed of a Partial Siamese Framework (PSF) and a Multiscale Bi-codec Information Fusion (MBIF) module. Specifically, the PSF is proposed to create dehazing prior information to guide the network to build Siamese constraints and achieve improved dehazing results. Furthermore, we design a MBIF module which can enhance feature extraction, and the multi-scale information is used to improve the reconstruction ability of the network for the color and texture of the dehazing image. Experimental results on challenging benchmark datasets demonstrate the superiority of our PSMB-Net over state-of-the-art image dehazing methods. The source code is available at https://github.com/thislzm/PSMB-Net.

Direction-of-arrival (DOA), also referred to as angle-of-arrival (AOA), is an excellent choice for unmanned aerial vehicle (UAV) localization and has garnered significant attention recently. In this article, we propose a novel two-dimensional (2D)-DOA auxiliary framework for anonymous UAV localization. At its core, this framework relies on measuring 2D-DOA using a monostatic multiple-input–multiple-output (MIMO) radar configured with electromagnetic vector sensors (EMVSs). Differing from existing mainstream methods, the multipath effect of the UAV is taken into account. A rearrangement multiple signal classification (R-MUSIC) algorithm is developed. The algorithm recovers the covariance matrix rank by connecting spatial responses from both transmitting (Tx) or receiving (Rx) arrays with radar cross-section (RCS) coefficients. Subsequently, rough 2D-DOA estimates are obtained using the vector cross-product (VCP) technique. These rough estimates are then used to establish good initialized values for refined 2-D spectral peak searching. Finally, leveraging the relationship between 2D-DOA and Tx/Rx array coordinates, a UAV’s 3-D position can be directly computed. This framework remains insensitive to the geometric configuration of Tx/Rx arrays while striking a balance between complexity and accuracy. Numerical simulation experiments confirm the improvements of our developed R-MUSIC algorithm.

Recently, the use of Unmanned Aerial Vehicle (UAV) imagery for object detection in forest fire detection has gained significant attention and has shown remarkable performance. However, most existing object detection models have neglected the exploration of relationships between positive sample features, which is crucial for learning more representative and color-robust features. Additionally, small objects in UAV images poses challenges in capturing sufficient object information and hinders accurate object detection. To address these issues, we propose FCLGYOLO that aims to constrain positive sample features and enrich the object information in the feature maps. Specifically, a Feature Invariance and Covariance Constraint (FICC) structure proposed to maintain feature invariance among positive samples and remove internal correlations. Furthermore, a Local Guided Global Module (LGGM) proposed to enrich object positioning and semantic information in the feature map, which leverages local features that focus on spatial information to facilitate the learning of global features that focus on frequency information. It is interesting to show that FCLGYOLO performs well even in the presence of heavy smoke or tree occlusions. Compared with multiple state-of-the-art object detection models on a forest fire dataset, experimental results demonstrate the superiority of FCLGYOLO. The code is available at: https://www.github.com/zhangshao249/FCLGYOLO .