Spectral–spatial feature modeling plays a crucial role in hyperspectral image (HSI) classification. However, existing models based on convolutional neural networks (CNNs) and Transformers still face a trade-off between feature modeling capability and computational efficiency. Although recent wavelet-based HSI classification methods have demonstrated the advantages of frequency-domain analysis, they typically rely on a single wavelet basis, which limits their ability to capture diverse spectral–spatial patterns across different frequency bands. To address these issues, we propose Wavelet Spectral-Spatial Mamba (WSSMamba) by combining wavelet transform with state space modeling for HSI classification. WSSMamba introduces an Adaptive Wavelet Fusion Module (AWFM) to perform multi-scale frequency domain decomposition using multiple wavelet bases. This allows the model to extract both low-frequency global structure and high-frequency local details. A Wavelet Feature Enhancement (WFE) module is also designed to improve feature discriminability by applying channel and spatial attention mechanisms. Furthermore, we propose a Spectral-Spatial Cross-Fusion Strategy (SSCFS), which uses multi-directional state modeling to dynamically integrate high-frequency information. Extensive experiments on benchmark datasets demonstrate that WSSMamba outperforms state-of-the-art methods in classification performance.

Automating video and radar spatial registration without sensor layout constraints is crucial for enhancing the flexibility of perception systems. However, this remains challenging due to the lack of effective approaches for constructing and utilizing matching information between heterogeneous sensors. Existing methods rely on human intervention or prior knowledge, making it difficult to achieve true automation. Consequently, establishing a registration model that automatically extracts matching information from heterogeneous sensor data remains a key challenge. To address these issues, we propose a novel Video-Radar Automatic Registration (VRAR) method based on vehicle trajectory spatiotemporal feature encoding and a bidirectional mapping network. We first establish a unified representation for heterogeneous sensor data by encoding spatiotemporal features of vehicle trajectories. Based on this, we automatically extract a large number of high-quality matching points from synchronized trajectory pairs using a frame synchronization strategy. Subsequently, we utilize the proposed Video-Radar Bidirectional Mapping Network to process these matching points. This network learns the bidirectional mapping between the two sensor modalities, extending the alignment from discrete local observation points to the entire observable space. Experimental results demonstrate that the VRAR method exhibits significant performance advantages in various traffic scenarios, verifying its effectiveness and generalizability. This capability of automated and adaptive registration highlights the method’s potential for broader applications in heterogeneous sensor integration.

The need for high-resolution (HR) remote sensing images has grown significantly in recent years as a result of the rapid advancement of fine-sensing technologies. However, increasing sensor resolution usually requires a costly investment. To tackle this challenge, super-resolution (SR) methods for remote sensing images have emerged as a cost-effective alternative to enhance the quality and usability of existing low-resolution (LR) images. Although many current methods have achieved some reconstruction results, they often suffer from problems such as transition smoothing and artifacts. To solve these problems, we propose an SR reconstruction model for detail recovery based on generative adversarial networks (GANs), referred to as DRGAN. Specifically, unlike the traditional residual-in-residual dense block network (RRDBNet), we propose a novel dense residual network (OSRRDBNet). It uses dynamic convolution and self-attention mechanisms to recover the rich detailed information in the image more effectively. In addition, we employ an average pooling layer to enhance the ability to capture HR image features. By conducting experiments on three different remote sensing datasets, DRGAN shows remarkable reconstruction results and successfully recovers the rich detail information in the images.

High resolution optical remote sensing images are the guarantee for remote sensing image analysis and application. However, many images suffer from blurring, distortion and low resolution due to camera hardware limitations and unstable image transmission. To address these challenges, we propose a Random Degradation and Adaptive Fusion-based super-resolution network (RDAF-GAN) for improving the clarity and detail of images. Specifically, unlike the traditional single degradation method, we design a comprehensive simulation model for remote sensing image degradation. It aims to generate low-resolution remote sensing images that are closer to the real scene. Subsequently, these generated low-resolution images are fed into RDAF-GAN for reconstruction to recover finer and more accurate image details. In addition, we propose an image fusion method based on local contrast. By adaptively adjusting the fusion weights, the perceived clarity and visual quality of the images are further enhanced. The experimental results validate that RDAF-GAN outperforms other state-of-the-art methods and consistently produces excellent results in a variety of situations.

Lane detection is crucial for autonomous driving systems (ADS), utilizing sensors like cameras and LiDAR to identify lanes and understand vehicle position, direction, and lane shape. It provides data support for the control system to make informed driving decisions. In this survey, we review recent advancements in lane detection, focusing on both 2D techniques and emerging 3D methods. We begin with an overview of the significance of lane detection in ADS, followed by an analysis of the evolution of 2D techniques over the past decade, covering traditional and deep learning approaches. We also examine recent advancements in 3D lane detection. Additionally, we summarize evaluation metrics and popular datasets in the field. Finally, we discuss current challenges and future directions in lane detection, aiming to provide valuable insights for researchers and developers in this technology.

3D lane detection provides richer spatial information than 2D lane detection planar position results. It improves vehicle perception in complex scenes, which is becoming increasingly important in intelligent driving. However, existing frameworks mainly focus on mapping front-view (FV) and bird’s-eye view (BEV) features and ignore the intrinsic correlations between different perspectives and scales. It can lead to incomplete feature extraction, affecting the perception accuracy of lane detection and adaptation ability to complex scenes. To alleviate these problems, we present a novel Freq-3DLane framework, an efficient end-to-end 3D lane detector. Instead of directly superimposing deeper and lower-level features, we propose a strategy for multi-scale information integration that exploits the frequency characteristics of features for image feature extraction. To enhance perception, we fuse image features at each scale through frequency processing to ensure that detailed information and global structure are fully utilized. Next, spatial transformation fusion captures the association between the FV and the BEV feature at any two-pixel position of both, thus enabling view feature transformation. In addition, attentional guidance enhances the lane semantic information to ensure recovery of the lane geometry for accurate 3D lane detection. Extensive results on two challenging benchmarks (Apollo 3D Lane Synthetic, and OpenLane) show that our model performs favorably against the state of the arts.

With the development of society, technological progress, and new needs, autonomous driving has become a trendy topic in smart cities. Due to technological limitations, autonomous driving is used mainly in limited and low-speed scenarios such as logistics and distribution, shared transport, unmanned retail, and other systems. On the other hand, the natural driving environment is complicated and unpredictable. As a result, to achieve all-weather and robust autonomous driving, the vehicle must precisely understand its environment. The self-driving cars are outfitted with a plethora of sensors to detect their environment. In order to provide researchers with a better understanding of the technical solutions for multi-sensor fusion, this paper provides a comprehensive review of multi-sensor fusion 3D object detection networks according to the fusion location, focusing on the most popular LiDAR and cameras currently in use. Furthermore, we describe the popular datasets and assessment metrics used for 3D object detection, as well as the problems and future prospects of 3D object detection in autonomous driving.