Plane segmentation is a basic yet important process in light detection and ranging (LiDAR) point cloud processing. The traditional point cloud plane segmentation algorithm is typically affected by the number of point clouds and the noise data, which results in slow segmentation efficiency and poor segmentation effect. Hence, an efficient encoding voxel-based segmentation (EVBS) algorithm based on a fast adjacent voxel search is proposed in this study. First, a binary octree algorithm is proposed to construct the voxel as the segmentation object and code the voxel, which can compute voxel features quickly and accurately. Second, a voxel-based region growing algorithm is proposed to cluster the corresponding voxel to perform the initial point cloud segmentation, which can improve the rationality of seed selection. Finally, a refining point method is proposed to solve the problem of under-segmentation in unlabeled voxels by judging the relationship between the points and the segmented plane. Experimental results demonstrate that the proposed algorithm is better than the traditional algorithm in terms of computation time, extraction accuracy, and recall rate.

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Change detection is one of the most important aspects of remote sensing applications. However, owing to the limited conditions of image acquisition, images obtained from the same type of remote sensors are usually used to monitor long-term land use and land cover (LULC) changes. Owing to developments in aerospace technology and new optical remote sensors, LULC change detection can be performed well with multisensor and multiresolution images. The main contribution of this article is to verify that it is feasible and practicable to perform long-term LULC change detection by applying different change detection methods to multisensor and multiresolution remote sensing images. In this study, different change detection methods were used on Landsat, QuickBird, WorldView-4, and GF-2 images to detect LULC changes on Weishui Campus of Chang'an University, China, from 1998 to 2018. Results showed that the direct spectral comparison method using Landsat-5 images could more efficiently detect LULC changes between 1998 and 2008 than the post-classification change detection method using Landsat-7 images. However, for 2008-2018, the object-based change detection method was more applicable than the post-classification method for monitoring LULC changes on campus by using time-series high-resolution images. This study can be used as a reference for the utilization of multisensor and multiresolution remote sensing images and the combination of different change detection methods in the LULC change detection field.

Using the principle of radar interferometry, we analyzed the temporal coherence changes of five typical ground features (residential areas, vegetation, bare soil, bridges, and factories and warehouses) in urban areas of Beijing using 29 images from Sentinel-1A equipped with a C-band synthetic aperture radar (SAR) sensor over one year. The results of the study showed the following. (1) Among the five typical ground features, the coherence of vegetation was the lowest. Owing to changes in its state and atmospheric conditions, the coherence of vegetation fluctuated sharply over the year. The coherence of factories and warehouses was the highest and relatively stable over the year. (2) Classifying the five typical ground features into artificial and natural features, we found that the artificial features of factories and warehouses, residential areas, and bridges maintained a high degree of coherence over the year. Among them, the coherence of residential areas was the most stable. The natural features of vegetation and bare soil were affected by the changes in their states and atmospheric conditions over the year. The research results can be used for the classification of land use types, the statistical analysis of urban green coverage, and the extraction of points with high coherence in long-term surface deformation inversion.