Monitoring water quality in turbid coastal waters is critical for ecological security, yet traditional sampling methods are often inefficient and spatially limited. Remote sensing, with its multiscale coverage and real-time capabilities, has emerged as a pivotal tool. However, the complex optical properties of turbid coastal waters, such as the strong absorption-scattering coupling effects of suspended particulate matter (SPM), chlorophyll-a ( $$Chl\text {-}a$$ C h l - a ), and colored dissolved organic matter (CDOM), pose significant challenges, including spectral signal saturation and poor algorithm generalizability. Current inversion approaches include: (1) physics-based models, such as the quasi-analytical algorithm (QAA) grounded in radiative transfer theory, which perform well in moderately turbid waters but are error-prone in highly turbid environments due to multiple scattering; (2) semi-empirical models, which leverage spectral band ratios for computational efficiency but suffer from limited regional adaptability; and (3) data-driven methods, including machine learning and deep learning, which improve accuracy via nonlinear mapping but face limitations in interpretability and robustness, particularly under extreme environmental conditions. Future efforts should prioritize multisource data fusion, integrating satellite, unmanned aerial vehicle (UAV), and in situ observations; physics-guided deep learning; hyperspectral technology optimization; and the development of standardized algorithm libraries. These advancements will enhance the accuracy, transferability, and operational feasibility of remote sensing-based water quality monitoring and contribute to more effective and sustainable marine governance.

By providing valuable data that allow scientists to study various oceanographic characteristics on a global scale, remote sensing techniques have considerably advanced our understanding of ocean fronts. Ocean fronts involve the interaction of water masses with specific physical properties such as temperature, sea color, salinity, and density. In particular, ocean fronts can act as barriers, impeding the movement of water masses and leading to the convergence or divergence of nutrients and marine species. Research on ocean fronts and their impact on marine biodiversity and physical environments has recently become popular. This paper introduces ocean front research progress based on remote sensing images, including research material, methods, limitations, and possible future research directions. The latest research on spatiotemporal variation in ocean fronts has substantially enhanced our understanding of the interaction of water masses with specific physical properties in the ocean.

Stereo matching plays a vital role in underwater environments, where accurate depth estimation is crucial for applications such as robotics and marine exploration. However, underwater imaging presents significant challenges, including noise, blurriness, and optical distortions that hinder effective stereo matching. This study develops two specialized stereo matching networks: UWNet and its lightweight counterpart, Fast-UWNet. UWNet utilizes self- and cross-attention mechanisms alongside an adaptive 1D-2D cross-search to enhance cost volume representation and refine disparity estimation through a cascaded update module, effectively addressing underwater imaging challenges. Due to the need for timely responses in underwater operations by robots and other devices, real-time processing speed is critical for task completion. Fast-UWNet addresses this challenge by prioritizing efficiency, eliminating the reliance on the time-consuming recurrent updates commonly used in traditional methods. Instead, it directly converts the cost volume into a set of disparity candidates and their associated confidence scores. Adaptive interpolation, guided by content and confidence information, refines the cost volume to produce the final accurate disparity. This streamlined approach achieves an impressive inference speed of 0.02 s per image. Comprehensive tests conducted in diverse underwater settings demonstrate the effectiveness of both networks, showcasing their ability to achieve reliable depth perception.

In autonomous driving, accurate 3-D multiobject tracking (MOT) plays a key role in ensuring vehicle safety. However, due to the complexity of the environment, existing methods still face many challenges when dealing with long-distance objects, partial occlusions, and interference from similar categories. To tackle these challenges, we propose a 3-D MOT framework based on a voxel masking encoder (VME) and a deep hashing paradigm (DHP). We introduce a masking strategy that processes voxel features from near to far while maintaining feature sparsity, effectively capturing global contextual information between spatial features. Simultaneously, DHP is utilized to generate image hash codes and compute their hamming distance from the category hash codes. This process effectively distinguishes between object categories and thus avoids cross-category object dissociation. In addition, we propose a distance optimization matching (DOM) method that uses geometric dimensions and spatial distances to build a cost matrix, achieving more efficient and precise object associations. Results from experiments conducted on the KITTI dataset reveal that our framework delivers outstanding tracking performance, surpassing other advanced methods in tracking accuracy. The code is released at https://github.com/lsy-collab/VD-MOT

Monitoring coastal ecosystems is essential for mitigating pollution, preserving biodiversity, and understanding the impacts of climate change. However, existing approaches, such as fully convolutional network (FCN) and Transformer-based models, often struggle with challenges such as low-class variance, difficulty in detecting small targets, and loss of boundary information. To handle large variations in target scales, we propose a semantic segmentation framework, SDFSeg, which integrates three key modules: the scale aware conv, dynamic deformable sample, and fusion perceiver. The scale aware conv is designed to improve multiscale feature extraction by incorporating convolutional layers with varying dilation rates; the dynamic deformable sample precisely aligns target boundaries, focuses on small features, and enables adaptive dynamic sampling for improved small target detection and boundary segmentation; and the fusion perceiver effectively fuses local and global information. Extensive experiments on benchmark datasets demonstrate that our method achieves a superior performance while reducing the computational overhead, confirming its practical applicability.