Classification of hypersaline water bodies, e.g., salt fields and salt lakes, presents unique challenges due to the similar spectral and scattering characteristics of various saline water bodies. To address this issue, we propose an innovative classification method tailored for such environments, integrating Sentinel-1 multitemporal polarimetric synthetic aperture radar (PolSAR) data with Landsat multispectral imagery. The method introduces a novel PolSAR-based feature, termed scattering mechanism entropy, to quantify variations in salt crystal precipitation processes. Additionally, the blue band from multispectral imagery is leveraged to represent ion concentrations in hypersaline water bodies. By deriving the statistical relationship between scattering mechanism entropy and blue band data for each pixel, we amplify the separability of salt features and mitigate the influence of spectral similarity. These derived features are then concatenated into a Chernoff distance-based classifier for improved classification performance. To validate the robustness and effectiveness of this method, we apply it to the classification of salt fields and salt lakes on four major salt lake sites in China: Qarhan salt lake (2018–2020), Yiliping salt lake (2021–2023), Taijnar salt lake (2019–2023), and Gasikule salt lake (2019–2023). The proposed approach achieves classification accuracies of 90.91%, 92.73%, 97.40%, and 97.65%, respectively, significantly outperforming existing water body classification methods.

This paper presents FAST-LIVO2, a fast and direct LiDAR-inertial-visual odometry framework designed for accurate and robust state estimation in SLAM tasks, enabling real-time robotic applications. FAST-LIVO2 integrates IMU, LiDAR, and image data through an efficient error-state iterated Kalman filter (ESIKF). To address the dimensional mismatch between LiDAR and image measurements, we adopt a sequential update strategy. Efficiency is further enhanced using direct methods for LiDAR and visual data fusion: the LiDAR module registers raw points without extracting features, while the visual module minimizes photometric errors without relying on feature extraction. Both LiDAR and visual measurements are fused into a unified voxel map. The LiDAR module constructs the geometric structure, while the visual module links image patches to LiDAR points, enabling precise image alignment. Plane priors from LiDAR points improve alignment accuracy and are refined dynamically during the process. Additionally, an on-demand raycast operation and real-time image exposure estimation enhance robustness. Extensive experiments on benchmark and custom datasets demonstrate that FAST-LIVO2 outperforms state-of-the-art systems in accuracy, robustness, and efficiency. Key modules are validated, and we showcase three applications: UAV navigation highlighting real-time capabilities, airborne mapping demonstrating high accuracy, and 3D model rendering (mesh-based and NeRF-based) showcasing suitability for dense mapping. Code and datasets are open-sourced on GitHub to benefit the robotics community.

A ground-based simulation system is necessary to verify the feasibility of real spaceborne Synthetic Aperture Radar (SAR) systems. Since echo simulation and image generation are computationally complex, parallel acceleration for SAR systems has been an active research area. However, most of the acceleration algorithms utilize compute unified device architecture (CUDA) as the programming platform, that works only on NVIDIA’s GPUs. This paper proposes a parallel optimization algorithm for spaceborne SAR echo simulation and imaging in Open Computing Language (OpenCL). In the echo simulation module, we optimize the Fast Fourier Transform (FFT), the calculation of range echoes, and the synchronization operation. In the imaging module, the data is decomposed to reduce the computational burden, and matrix transpose and phase factor multiplication are optimized using OpenCL. The architecture and steps needed to extract parallelism in the implementation of the algorithm for accelerated SAR echo simulation and imaging are described in detail. Benefiting from the better generality of OpenCL, this algorithm can be used to program General-Purpose Graphics Processing Units (GPGPU), i.e., CPUs, GPUs, and other types of processors. The performance is promoted by orders of magnitude compared with CPU-based implementation.

Multi-temporal PolSAR data captures the temporal variations in polarization parameters, enabling more accurate land cover classification. Most existing multi-temporal PolSAR features rely on comparing only two-time points. These approaches can be limited in capturing cumulative changes over a longer period. To better represent the cumulative changes of land cover in the entire time-series, this paper proposes a multi-temporal PolSAR feature, namely time correlation entropy. We first extract the dominant scattering mechanism of targets from the polarization covariance matrices using matrix decomposition. Then the time correlation matrix is constructed by comparing all dominant scattering mechanism pairs in the time series. From the Shannon entropy, the entropy of the time correlation matrix, i.e., time correlation entropy, is derived to indicate the degree of changes in the land cover during the observation period. Finally, the maximum entropy principle is further applied to prove that this entropy conforms to a normal distribution. Following this corollary, a classification method based on the interval estimation of distribution parameters is proposed. We evaluate the proposed feature and classification on the salt lake classification application in Qarhan Salt Lake and Gasikule Salt Lake using Sentinel-1 images. Compared to common PolSAR features and classification methods, our method gains the best results. Besides, its results also have better regional consistency and noise resistance.

We propose a novel interactive semantic shared control framework that exploits an active high-level communication loop between the human operator and the robot for time-efficient teleoperation. In shared control approaches, accurate prediction of the operator’s intention is crucial to enable the robot to provide meaningful assistance. Incorrect intention prediction (e.g., target objects to be interacted with) increases the task duration due to conflicts between human behaviors and robot guidance. Unlike existing methods, our approach not only passively observes and predicts the human operator’s input in the haptic control loop, but also actively communicates with the human operator in an additional semantic loop in the form of a speech user interface to optimize the effectiveness of assistance. We evaluate our ISSC framework for a pegin-hole teleoperation task. The experimental results show that the proposed framework significantly outperforms teleoperation without assistance and conventional shared control paradigms regarding task execution efficiency and user control quality, and reduces task completion time by up to 26.68% and 39.00%, respectively.