Hyperspectral image classification faces significant challenges due to high-dimensional spectral redundancy and complex spatial-spectral dependencies. While existing MambaHSI models leverage state-space modeling to enhance representation learning, their unidirectional formulation fails to fully capture bidirectional spatial interactions and cross-band contextual dependencies. Moreover, the uniform projection mechanism struggles to effectively distinguish spectral variations across different wavelengths. To address these limitations, we propose MambaHSI+, a novel framework that integrates bidirectional state-space modeling with spectral trajectory learning. The proposed architecture introduces three key innovations: (1) A bidirectional context modeling module, enhanced by reverse-order modeling, enables multi-directional spatial information propagation through recursive state transitions, facilitating comprehensive local-global feature aggregation while maintaining linear computational efficiency; (2) a spectral trajectory learning paradigm that formulates spectral evolution as continuous state-space process with bidirectional propagation, effectively encoding cross-band relationships; and (3) a Mamba-enhanced channel attention mechanism that adaptively emphasizes discriminative spectral features via selective state-space transformations. Extensive experiments on four benchmark datasets demonstrate state-of-the-art performance, achieving an average accuracy improvement of 2.51% over MambaHSI. By integrating state-space systems with advanced Mamba mechanisms, MambaHSI+ establishes a new paradigm for spectral-spatial representation learning, significantly advancing classification accuracy while ensuring computational efficiency. The latest logs are now available at https://github.com/RockAilab/MambaHSI_Plus.

Registration will directly affect the quality of overall rock-mass point cloud, which is the basis of 3-D reconstruction for rock mass. Advanced methods establish correspondence by extracting various features that remain unchanged. Although these methods have made great progress, they analyze the local characteristics of each sample point, which leads to be inefficient. In this article, we select registration interesting points from feature lines that were extracted based on supervoxel and innovatively introduce the “clustering, primary matching, and coarse registration” strategy, which effectively reduces the complexity of calculating the corresponding relationship during point cloud registration. Finally, the iterative closest point (ICP) algorithm is used to optimize the result of coarse registration. By selecting registration interesting points from the extracted feature lines, the proposed method inherits the robustness of feature lines to noise, initial position, and so on. The experimental results prove that the coarse registration and refined registration results of the proposed method both have high accuracy and efficiency.

Point cloud registration is one of the basic research hotspots in the field of 3D reconstruction. Although many previous studies have made great progress, the registration of rock point clouds remains an ongoing challenge, due to the complex surface, arbitrary shape, and high resolution of rock masses. To overcome these challenges, a novel registration method for rock point clouds, based on local invariants, is proposed in this paper. First, to handle the massive point clouds, a point of interest filtering method based on a sum vector is adopted to reduce the number of points. Second, the remaining points of interest are divided into several cluster point sets and the centroid of each cluster is calculated. Then, we determine the correspondence between the original point cloud and the target point cloud by proving the inherent similarity (using the trace of the covariance matrix) of the remaining point sets. Finally, the rotation matrix and translation vector are calculated, according to the corresponding centroids, and a correction method is used to adjust the positions of the centroids. To illustrate the superiority of our method, in terms of accuracy and efficiency, we conducted experiments on multiple datasets. The experimental results show that the method has higher accuracy (about ten times) and efficiency than similar existing methods.

Existing 3-D object extraction methods on terrestrial laser point clouds are further developed through filtering and labeling. However, such predefined features are heuristically designed to process generic object point clouds. Thus, existing abilities are insufficient to handle specific rock-mass point clouds. Given the complexity and diversity of terrestrial environments, the effective removal of vegetation points from rock-mass point clouds is particularly challenging. To address such problems, this study presents a novel approach for 3-D rock-mass point clouds labeling by using convolutional feature learning based on distribution priors with multiple scales and views. First, to extract discriminative features of each point for classification, we propose novel multiview supporting planes to analyze the spatial distribution and structure of its neighboring points for each category. Second, we define the multiscale spatial distribution matrix on a grid representation (e.g., the number of points projected into each cell). Last, the statistical information of points is nonlinearly combined and hierarchically compressed to generate a compact and effective convolutional feature representation for classification. The effectiveness of the proposed method is evaluated via experiments on rock-mass point clouds from different scenes. Compared with existing extraction approaches, experimental results indicate the superiority of the proposed method in terms of the precision and recall.

Hyperspectral image (HSI), with their rich spectral information and spatial details, have demonstrated significant potential for classification tasks in fields such as remote sensing, agriculture, and environmental monitoring. However, existing methods still exhibit limitations in feature representation, primarily manifested in insufficient contextual modeling and the inability to effectively address spectral redundancy and significant variations in spatial scales. To address these challenges, this paper proposes an enhanced MambaHSI-based framework for HSI classification, focusing on improving the representation capability of spatial-spectral features. The proposed method consists of three key innovations: (1) A hierarchical DualGroupMamba module that progressively models intra-group and inter-group spectral dependencies to enhance fine-grained spectral discrimination and global contextual awareness; (2) a lightweight Hyperspectral Channel Attention (HCA) that dynamically adjusts the importance of feature channels based on the spatial–spectral information of different bands, effectively suppressing redundant information and highlighting discriminative features. (3) a Hybrid Feature Enhancer (HFE) module that effectively represents and fuses multi-scale spatial features by extracting local texture details and perceiving the overall spatial distribution of scenes, thereby enhancing the model’s adaptability to complex spatial structures. Through a systematic evaluation on four benchmark hyperspectral datasets, the proposed method achieved an average overall classification accuracy of 95.64%, outperforming the current best method by 1.89%. The experimental results validate the superior performance of the proposed approach in enhancing the representation of spatial–spectral features. The latest logs are now available at https://github.com/Tomyaya/EFR.