Automatic image stitching and high-precision photogrammetry have been active research fields. This article proposes a novel stitching method for high-precision, low-overlap thermal infrared array scanning (TIRAS) images. The approach corrects the TIRAS camera’s internal parameters, external orientation (EO) elements, and planar projection errors by calculating three homography matrices. First, we establish collinearity equations using the calibrated internal parameters and EO elements with errors and further compute the direct projection transformation model of the original image. Then, we propose a global error optimization model based on a fixed framework according to the midpoints of the alignment point pairs of the correcting image and neighboring images. The homography matrix calculated by this model can relatively correct the EO element errors in the directly projected image. Finally, we calculate the homography matrix for the planar projection error correction using the universal transverse mercartor (UTM) coordinates of geometric targets and their corresponding image coordinates and use the matrix to perform absolute error correction on the stitched wide-field image of the reprojected image. Comparing the proposed method with AutoStitch, Liu’s, and PTGui for mid-wave infrared (MWIR) image stitching results, it exhibits superior image stitching quality, without noticeable distortion or stretching, and with intact object structures. Besides, our method demonstrates outstanding positioning accuracy. Without ground control points, the optimal positioning accuracy is 0.7538 m (RMSE), less than 1 pixel. In conclusion, the proposed stitching method showcases stable, automated, and highly accurate global error optimization capabilities, and it is also suitable to splice array remote sensing images equipped with a high-accuracy POS system.

When applied across different scenes, hyperspectral image (HSI) classification models often struggle to generalize due to the data distribution disparities and labels’ scarcity, leading to domain shift (DS) problems. Recently, the high-level semantics from text has demonstrated the potential to address the DS problem, by improving the generalization capability of image encoders through aligning image-text pairs. However, the main challenge still lies in crafting appropriate texts that accurately represent the intricate interrelationships and the fragmented nature of land cover in HSIs and effectively extracting spectral-spatial features from HSI data. This article proposes a domain generalization (DG) method, EHSnet, to address these issues by leveraging multilayered explicit high-level semantic (EHS) information from different types of texts to provide precisely relevant semantic information for the image encoder. A multilayered EHS information paradigm is well-defined, aiming to extract the HSI’s intricate interrelationships and the fragmented land-cover features, and a dual-residual encoder connected by a 2-D convolution is designed, which combines CNNs with residual structure and Vision Transformers (ViTs) with short-range cross-layer connections to explore the spectral-spatial features of HSIs. By aligning text features with image features in the semantic space, EHSnet improves the representation capability of the image encoder and is endowed with zero-shot generalization ability for cross-scene tasks. Extensive experiments conducted on three hyperspectral datasets, including Houston, Pavia, and XS datasets, validate the effectiveness and superiority of EHSnet, with the Kappa coefficient improved by 8.17%, 3.22%, and 3.62% across three datasets compared to the state-of-the-art (SOTA) methods. The code is available athttps://github.com/SCUT-CCNL/EHSnet.

The current Automatic Emergency Braking (AEB) system based on vehicle sensors has a field of view blind spot, greatly limiting its function. This paper proposes an optimization strategy for commercial vehicle AEB system based on unsafe control behavior to improve the safety and reliability of the AEB system. Firstly, the communication delay law of vehicle-to-vehicle communication under different working conditions is obtained through real vehicle tests. The delay is then used to compensate and correct parameters such as speed, displacement, and coordinates of the environmental vehicle, in order to account for the impact of communication delay on system decision-making. Next, an AEB strategy for commercial vehicles at the intersection section is formulated. When two vehicles are about to collide, the braking system of the test vehicle is controlled to automatically emergency brake with the maximum braking deceleration to avoid collision. The AEB system strategy is optimized based on the analysis of unsafe control behavior. Then, an improved Antenna historical optimum-based Beetle Antenna Search (ABAS) algorithm is proposed based on the Beetle Antenna Search (BAS) and Beetle Swarm Antenna Search (BSAS) algorithm, which improves the optimization performance of the algorithm under the known constraint space, such as vehicle motion modeling and collision avoidance. Finally, the simulation result show that our proposed method can effectively prevent the collision of two vehicles at the intersection, and has high safety and reliability.

Hyperspectral LIDAR (HSL) is an innovative active remote sensing technology that allows for the simultaneous collection of spectral and spatial information. In this study, we primarily focus on the radiation correction method of the incident angle and distance effects for the backscatter intensity of HSL. We have developed a comprehensive radiometric correction model that addresses these effects. Additionally, we have applied the correction model to point cloud classification using the random forest method. Comparing the accuracy of point cloud classification before and after correction, we observed a 9.6% improvement in overall accuracy (OA) and a 10.8% improvement in the kappa coefficient. These results indicate that the radiometric correction model significantly enhances the classification accuracy.

Convolutional neural networks (CNN) are capable of extracting features with high accuracy, which is dominant in visual-based classification. Previous researches demonstrate that CNN can extract essential features of the target in the plant feature extraction and classification. Hyperspectral LIDAR (HSL) is a novel active remote sensing technology that can simultaneously collect spectral and spatial information. This paper proposed a novel classification method named VI-CNN for hyperspectral LiDAR, which combines the spectral features with the vegetable index(VI). As far as we know, we are the first to apply CNN to HSL data classification. The VI -CNN is divided into two parts. Firstly, spectral CNN focuses on intra-spectral correlations; secondly, the vegetation indices supplement the biological parameters. The evaluation shows that the concatenation has stronger identification and robustness than standalone methods. The experimental results demonstrate that the VI-CNN significantly improves the classification accuracy against other traditional machine-learning methods.

Position and location constitute critical context for Internet of Things (IoT) devices. Global navigation satellite systems (GNSSs) are the primary apparatus providing precise position and location information for IoT devices in outdoor environments. However, in dense urban areas, non-line-of-sight (NLOS) signals will induce large errors in GNSS pseudorange measurements due to the additional signal transmission paths. The vector tracking (VT) technique utilizing a Kalman filter (KF) to estimate navigation solutions has been investigated in NLOS detection, and its advantages have been demonstrated. However, the estimation of NLOS-induced bias has not been thoroughly investigated in the VT framework. In this article, we focus on the estimation and correction of NLOS-induced errors within the VT framework. First, graph optimization (GO) instead of a KF is incorporated with VT to optimize the estimation of navigation solutions. The NLOS-induced bias is then added to the VT state vector as the variable for real-time estimation. Compared with the KF-VT method, in GO-VT, the state transformation and the measurement model are regarded as constraints to optimize the state vector estimation. Hence, the GO-VT framework is more flexible than the KF approach in dealing with state vector changes. An iterative process is conducted to solve for the optimization results; a multiple-correlator scheme is employed in GO-VT to provide the initial values of the NLOS-induced bias. Three collected GPS L1 data sets (static and dynamic) are used to evaluate the proposed method. The statistical results support the conclusion that GO-VT with state augmentation achieves superior position estimation in urban areas.

Stripe noise still affects full-spectrum airborne hyperspectral imager (FAHI) images after laboratory radiometric calibration, which seriously affects the subsequent applications of the imager. Therefore, two state-of-the-art methods, median linear correction (MLC) and Fourier transform filtering (FTF), were proposed to restore FAHI images, and the residual stripes were removed in most cases. However, these methods have their own limitations. For instance, the restored image has a slight “shadow” in cases where the high-response digital numbers (DNs) of the detector are aligned with the flight direction. This letter proposes a new method based on improved statistics to restore FAHI images. In this method, the hyperspectral image data from the adjacent flight paths is used to obtain the uniform response DNs for nearly identical low and high irradiances. Subsequently, a statistics-based MLC method is used to eliminate the stripe noise. To quantitatively evaluate the restoration results, we compared results with MLC and FTF methods. The change in mean value and mean relative deviation of the proposed method for the high-response DNs area of the image is 0.25% and 0.97%, respectively, better than that of the other two methods. The experimental results demonstrate that the proposed method is effective for removing stripe noise and preserving accurate image information of push-broom hyperspectral imagery.

Airborne hyperspectral images are used for crop identification with a high classification accuracy because of their high spectral resolution, spatial resolution, and signal-to-noise ratio (SNR). However, the tradeoffs between the three core parameters of a hyperspectral imager (SNR, spatial resolution, and spectral resolution) should be considered for designing an efficient imaging system. Only a few reported studies on the analysis of the impact of SNR on identification accuracy are available. Further, the tradeoffs and mutual interactions among these parameters are rarely considered. In this empirical study, our aim was to understand the relationship among the core parameters and their effects on crop identification accuracy by analyzing the tradeoffs and mutual interactions among these parameters. We analyzed the hyperspectral images of a typical plain agricultural area in Xiongan, China, acquired by the newly developed sensor airborne multimodular imaging spectrometer (AMMIS). The fundamental images were transformed to form datasets with different ranges of spectral resolution, spatial resolution, and SNR using data reconstruction methods. We adopted the classification and regression tree (CART), random forest (RF), and k-nearest neighbor (kNN) classifiers, and observed the overall accuracy (OA) across the degraded hyperspectral datasets. The experimental results indicated that the OA decreased with a decreasing SNR. As the spectral resolution became coarser, the OA first increased, plateaued, and then decreased. However, the OA increased with decreasing spatial resolution. This study was performed with the goal of bridging the knowledge gap between the back-end hyperspectral sensor designing and its front-end applications.

Following calibration, hyperspectral images remain affected by spatial dimension nonuniformity, i.e., stripe noise, due to stray light interference, slit contamination, and instrument instability. The full spectrum airborne hyperspectral imager (FAHI) is a Chinese next-generation pushbroom sensor with a spectral range covering the visible near-infrared, shortwave-infrared (SWIR), and thermal infrared regions with spectral sampling intervals of 2.34, 3, and 32 nm, respectively. However, the residual stripe noise remains in FAHI images after relative radiometric correction based on the laboratory calibration, especially in low signal-to-noise ratio bands. To solve this problem, a new technique combining image statistics and spatial filtering algorithms has been developed for FAHI image correction. In this method, image statistics are obtained to calculate the gain and offset of each pixel for image nonuniformity correction. Then, a spatial filter removes the residual stripes. This paper presents the principles of this method along with details of validation experiments and results. To validate the effectiveness of the proposed method, comparison with two destriping methods and quantitative analyses are carried out. Moreover, the method is applied to an SWIR hyperspectral image from the TianGong-1 spacecraft, yielding good results. The experimental results suggest that the proposed method is convenient and practical for improving the relative radiometric accuracy of airborne/spaceborne hyperspectral images.