We implemented a change detection method based on a multitemporal object using a chi-square method and iterative trimming to find the changed object in the past. Trapping in the sample data does not obey Gaussian distribution, and the detection result is not ideal. To fix this problem, we proposed a method based on the law of cosines with a box-whisker plot. First, using the incremental segmentation method to segment the image and selecting the optimum combination of spatial, spectral, and contextual features by the Optimum Index Factor method, we constructed the feature space of different time images. Then, the law of cosines was used to calculate the change index of every object. Finally, we identified changed objects by analyzing the change index using the box-whisker plot. High-resolution remote sensing images of GF-1 were used as the experimental data. The experimental results show that the correct detection accuracy and omissions rate accuracy are much better than the results of the traditional multitemporal-object-based change detection.

The digital elevation model (DEM) generates a digital simulation of ground terrain in a certain range with the usage of 3D point cloud data. It is an important source of spatial modeling information. Due to various reasons, however, the generated DEM has data holes. Based on the algorithm of deep learning, this paper aims to train a deep generation model (DGM) to complete the DEM void filling task. A certain amount of DEM data and a randomly generated mask are taken as network inputs, along which the reconstruction loss and generative adversarial network (GAN) loss are used to assist network training, so as to perceive the overall known elevation information, in combination with the contextual attention layer, and generate data with reliability to fill the void areas. The experimental results have managed to show that this method has good feature expression and reconstruction accuracy in DEM void filling, which has been proven to be better than that illustrated by the traditional interpolation method.

<!-- *** Custom HTML *** --> In order to overcome errors caused by season, shooting angle, and other factors in multitemporal remote sensing image change detection, a method based on old-temporal vector data and new-temporal image data is proposed under the premise that changes of images are normally less than the unchanged ones. Getting the object through incremental segmentation under the constraints of the previous vector data, we extract its textural and spectral features to get the dataset by the transform of principal component analysis. After this, the isolation forest method is used to calculate the object’s change index, and the change threshold is obtained by the Bayes method. We conduct two experiments. The effectiveness of the proposed method was verified by comparing image–image and vector–image change detection methods as well as Mahalanobis distance and isolation forest change methods for which the accuracy rate of experiment 1 is 92.35% and that of experiment 2 is 93.18%.

This paper outlines a low-cost, user-friendly photogrammetric technique with nonmetric cameras to obtain excavation site digital sequence images, based on photogrammetry and computer vision. Digital camera calibration, automatic aerial triangulation, image feature extraction, image sequence matching, and dense digital differential rectification are used, combined with a certain number of global control points of the excavation site, to reconstruct the high precision of measured three-dimensional (3-D) models. Using the acrobatic figurines in the Qin Shi Huang mausoleum excavation as an example, our method solves the problems of little base-to-height ratio, high inclination, unstable altitudes, and significant ground elevation changes affecting image matching. Compared to 3-D laser scanning, the 3-D color point cloud obtained by this method can maintain the same visual result and has advantages of low project cost, simple data processing, and high accuracy. Structure-from-motion (SfM) is often used to reconstruct 3-D models of large scenes and has lower accuracy if it is a reconstructed 3-D model of a small scene at close range. Results indicate that this method quickly achieves 3-D reconstruction of large archaeological sites and produces heritage site distribution of orthophotos providing a scientific basis for accurate location of cultural relics, archaeological excavations, investigation, and site protection planning. This proposed method has a comprehensive application value.