The detection of camouflaged targets in the complex background is a hot topic of current research. Existing hyperspectral target detection algorithms do not take advantage of spatial information and rarely use temporal information. It is difficult to obtain the required targets, and the detection performance in hyperspectral sequences with complex background will be low. Therefore, a hyperspectral time-series target detection method based on spectral perception and spatial-temporal tensor decomposition (SPSTT) is proposed. Firstly, a sparse target perception strategy based on spectral matching is proposed. To initially acquire the sparse targets, the matching results are adjusted by using the correlation mean of the prior spectrum, the pixel to be measured and the four-neighborhood pixel spectra. The separation of target and background is enhanced by making full use of local spatial structure information through local topology graph representation of the pixel to be measured. Secondly, in order to obtain a more accurate rank and make full use of temporal continuity and spatial correlation, a spatial-temporal tensor model based on the Gamma norm and L _2,1 norm is constructed. Furthermore, an excellent alternating direction method of multipliers is proposed to solve this model. Finally, spectral matching is fused with spatial-temporal tensor decomposition in order to reduce false alarms and retain more right targets. A 176-band hyperspectral image sequence (BIT-HSIS-I) dataset is collected for the hyperspectral target detection task. It is found by testing on the collected dataset that the proposed SPSTT has superior performance over the state-of-the-art algorithms.

Hyperspectral target detection technology is becoming more and more important in remote sensing. Most of the traditional methods of target sensing using spectral information treat hyperspectral image as 2-D matrix regardless of the structure information of hyperspectral image, resulting in insufficient separation of target and background and limited detection accuracy. In order to better utilize the hyperspectral intrinsic structural information, this article proposes a hyperspectral target sensing approach based on tensor adaptive reconstruction cascaded with global and local feature fusion (TRGLF). First, the Tucker decomposition and reconstruction method is utilized to alleviate the influence of noise and other elements in the complex background hyperspectral, which can maintain the intrinsic structural features of the data and improve the background and target separation. The principal components of the factor matrix are determined by a logarithmic singular value summation strategy, and then the energy difference between the spectra to be measured and the previous spectra is calculated to fine-tune the principal components and obtain more appropriate principal component values. Second, on the basis of reconstructed data, a global and local spatial spectral fusion method is adopted to obtain the target of interest. This includes using globally constrained energy minimization to obtain the target of interest and using local differential sensing to further obtain the target boundary. The detection performed on four realistically acquired hyperspectral water surface datasets demonstrate the excellent detection performance of the proposed target detection method.

In the field of intelligent transportation systems, pedestrian detection has become a problem that is urgently in need of a solution. Effective pedestrian detection reduces accidents and protects pedestrians from injuries. A pedestrian-detection algorithm, namely, single template matching with kernel density estimation clustering (STM-KDE), is proposed in this paper. First, the KDE-based clustering method is utilized to extract candidate pedestrians in point clouds. Next, the coordinates of the point clouds are transformed into the pedestrians’ local coordinate system and projection images are generated. Locally adaptive regression kernel features are extracted from the projection image and matched with the template features by using cosine similarity, based on which pedestrians are distinguished from other columnar objects. Finally, comparative experiments using KITTI datasets are conducted to verify pedestrian-detection performance. Compared with the STM with radially bounded nearest neighbor (STM-RBNN) algorithm and the KDE-based pedestrian-detection algorithm, the proposed algorithm can segment gathering pedestrians and distinguish them from other columnar objects in real scenarios.

Point cloud semantic segmentation can enhance the understanding of the production environment and is a crucial component of vision tasks. The efficacy and generalization prowess of deep learning-based segmentation models are inherently contingent upon the quality and nature of the data employed in their training. However, it is often challenging to obtain data with inter-class balance, and training an intelligent segmentation network with the imbalanced data may cause cognitive bias. In this paper, a network framework InvSpaceNet is proposed, which generates an inverse feature space to alleviate the cognitive bias caused by imbalanced data. Specifically, we design a dual-branch training architecture that combines the superior feature representations derived from instance-balanced sampling data with the cognitive corrections introduced by the proposed inverse sampling data. In the inverse feature space of the point cloud generated by the auxiliary branch, the central points aggregated by class are constrained by the contrastive loss. To refine the class cognition in the inverse feature space, features are used to generate point cloud class prototypes through momentum update. These class prototypes from the inverse space are utilized to generate feature maps and structure maps that are aligned with the positive feature space of the main branch segmentation network. The training of the main branch is dynamically guided through gradients back propagated from different losses. Extensive experiments conducted on four large benchmarks (i.e., S3DIS, ScanNet v2, Toronto-3D, and SemanticKITTI) demonstrate that the proposed method can effectively mitigate point cloud imbalance issues and improve segmentation performance.

Lidar has received a lot of attention due to its precise ranging accuracy. Ground points filtering is an important task in point clouds processing. It's a challenge to model the ground surface and filter the point clouds accurately in the case of complex ground undulations, occlusions, and sparse point clouds. A novel ground surface modeling method based on a hybrid regression technique is proposed in this paper. The method integrates Gaussian process regression (GPR) and robust locally weighted regression (RLWR) by dividing the point clouds that are projected on the polar grid map into radial and circumferential filtering processes to form a hybrid regression model, which has the ability to eliminate the influence of outliers and model the ground surface robustly. First, the RLWR combined with gradient filter is applied to fit the sampled points in the radial direction, which will exclude outliers and get the fitting ground line. All radial fitting lines constitute the seed skeleton of the whole plane. Then, based on the seeds in the same circumferential of the skeleton, the GPR is applied to construct the ground surface model. The comparative experiments are implemented quantitatively and qualitatively on the simulated point clouds and measured data. The results show that the proposed method performs well in most real scenarios, even in the cases of ground undulation, occlusion, and sparse point clouds.

To significantly enhance the performance of point cloud semantic segmentation, this manuscript presents a novel method for constructing large-scale networks and offers an effective lightweighting technique. First, a latent point feature processing (LPFP) module is utilized to interconnect base networks such as PointNet++ and Point Transformer. This intermediate module serves both as a feature information transfer and a ground truth supervision function. Furthermore, in order to alleviate the increase in computational costs brought by constructing large-scale networks and better adapt to the demand for terminal deployment, a novel point cloud lightweighting method for semantic segmentation network (PCLN) is proposed to compress the network by transferring multidimensional feature information of large-scale networks. Specifically, at different stages of the large-scale network, the structure and attention information of the point features are selectively transferred to guide the compressed network to train in the direction of the large-scale network. This paper also solves the problem of representing global structure information of large-scale point clouds through feature sampling and aggregation. Extensive experiments on public datasets and real-world data demonstrate that the proposed method can significantly improve the performance of different base networks and outperform the state-of-the-art.