LiDAR and photogrammetry are active and passive remote sensing techniques for point cloud acquisition, respectively, offering complementary advantages and heterogeneous. Due to the fundamental differences in sensing mechanisms, spatial distributions, and coordinate systems, their point clouds exhibit significant discrepancies in density, precision, noise, and overlap. Coupled with the lack of ground truth for large-scale scenes, integrating the heterogeneous point clouds is a highly challenging task. This article proposes a self-supervised registration network based on a masked autoencoder, focusing on heterogeneous LiDAR and photogrammetric point clouds. At its core, the method introduces a multiscale masked training strategy to extract robust features from heterogeneous point clouds under self-supervision. To further enhance registration performance, a rotation-translation embedding module is designed to effectively capture the key features essential for accurate rigid transformations. Building upon robust representations, a transformer-based architecture seamlessly integrates local and global features, fostering precise alignment across diverse point cloud datasets. The proposed method demonstrates strong feature extraction capabilities for both LiDAR and photogrammetric point clouds, addressing the challenges of acquiring ground truth at the scene level. Experiments conducted on two real-world datasets validate the effectiveness of the proposed method in solving heterogeneous point cloud registration problems.

Multispectral point cloud is a novel type of data rich in spectral and spatial information. 3D reconstruction is a low-cost solution for acquiring multispectral point cloud. However, most of the existing methods have been developed for RGB images, which are inapplicable to multispectral images due to the special structure of multispectral sensors and the nonlinear intensity differences. In this paper, a robust 3D reconstruction method for multispectral images is proposed to generate multispectral point cloud by harnessing their spatial and spectral information. Considering the characteristics of multispectral image acquisition, reflectance correction and band alignment steps are introduced into the proposed method, aiming to reduce the impact of band differences and spatial errors on 3D reconstruction. Subsequently, a fused multispectral feature extraction is employed to provide more potential reconstruction feature points. To reduce the mismatched feature points induced by the spectra of vegetation regions, an NDVI-guided feature matching algorithm is proposed that provides accurate correspondence calculation for multispectral images reconstruction. The experiments compared with several well-known methods and a commercial software on two datasets have shown superior reconstruction performance.

UAV platforms equipped with hyperspectral sensors are widely welcomed in various fields for rich spectral and spatial information. This paper presents a method for generating true digital orthophoto map of push-broom hyperspectral scanners assisted by Lidar, which is suitable for UAV platforms with low flight altitude. Different from the traditional digital orthophoto map generation method, this method utilizes Lidar to obtain the elevation information lost in the hyperspectral imaging process. GNSS/INS unit is used for direct georeferencing to remove geometric distortions caused by platform instability. Furthermore, we reduce the projection errors caused by sudden changes in elevation assisted by Lidar data. The UAV flight test in urban scenes verifies the effectiveness and accuracy of the method.

Unmanned aerial vehicles (UAVs) equipped with hyperspectral scanners and LiDARs can flexibly acquire rich spectral and geometric information about the observation scene. To combine the complementary advantages of multi-source data, the stereo registration of hyperspectral images and LiDAR data has become one of the hot topics in remote sensing community. However, existing research works are more focused on exploiting intensity information from multi-source data, which is applicable to data acquired on manned vehicle platforms or satellite platforms. For UAV platforms with poor stability and limited load, the low signal-to-noise ratio of LiDAR data and the complex distortion of push-broom images bring great challenges to stereo registration. Under this circumstance, an intensity-independent stereo registration method is proposed in this paper, which is based on the physical model of the integrated system and the sensor detection principles. Specifically, the proposed method utilizes the position and orientation system (POS) to reduce the impact of UAV platform motion on hyperspectral imaging, and projection errors are eliminated by the ray tracing model with aid of LiDAR data. Finally, a virtual ray decomposition model based on geometric features is constructed to realize the stereo registration of hyperspectral images and LiDAR data. Compared with an advanced solution and professional processing software, the proposed method has shown better registration performance on two data of different scenarios.