No abstract

Oblique imagery obtained from an Unmanned Aerial Vehicle (UAV) has been widely applied to large-scale three-dimensional (3D) reconstruction; however, the problems of partially missing model details caused by such factors as occlusion, distortion, and airflow, are still not well resolved. In this paper, a loop-shooting-aided technology is used to solve the problem of details loss in the 3D model. The use of loop-shooting technology can effectively compensate for losses caused by occlusion, distortion, or airflow during UAV flight and enhance the 3D model details in large scene- modeling applications. Applying this technology involves two key steps. First, based on the 3D modeling construction process, the missing details of the modeling scene are found. Second, using loop-shooting image sets as the data source, incremental iterative fitting based on aerotriangulation theory is used to compensate for the missing details in the 3D model. The experimental data used in this paper were collected from Yunnan Normal University, Chenggong District, Kunming City, Yunnan Province, China. The experiments demonstrate that loop-shooting significantly improves the aerotriangulation accuracy and effectively compensates for defects during 3D large-scale model reconstruction. In standard-scale distance tests, the average relative accuracy of our modeling algorithm reached 99.87% and achieved good results. Therefore, this technique not only optimizes the model accuracy and ensures model integrity, but also simplifies the process of refining the 3D model. This study can be useful as a reference and as scientific guidance in large-scale stereo measurements, cultural heritage protection, and smart city construction.

Abstract:Change in urban construction land use is an important factor when studying urban expansion. Many scholars have combined cellular automata (CA) with data mining algorithms to perform relevant simulation studies. However, the parameters for rule extraction are difficult to determine and the rules are simplex, and together, these factors tend to introduce excessive fitting problems and low modeling accuracy. In this paper, we propose a method to extract the transformation rules for a CA model based on the Classification and Regression Tree (CART). In this method, CART is used to extract the transformation rules for the CA. This method first adopts the CART decision tree using the bootstrap algorithm to mine the rules from the urban land use while considering the factors that impact the geographic spatial variables in the CART regression procedure. The weights of individual impact factors are calculated to generate a logistic regression function that reflects the change in urban construction land use. Finally, a CA model is constructed to simulate and predict urban construction land expansion. The urban area of Xinyang City in China is used as an example for this experimental research. After removing the spatial invariant region, the overall simulation accuracy is 81.38% and the kappa coefficient is 0.73. The results indicate that by using the CART decision tree to train the impact factor weights and extract the rules, it can effectively increase the simulation accuracy of the CA model. From convenience and accuracy perspectives for rule extraction, the structure of the CART decision tree is clear, and it is very suitable for obtaining the cellular rules. The CART-CA model has a relatively high simulation accuracy in modeling urban construction land use expansion, it provides reliable results, and is suitable for use as a scientific reference for urban construction land use expansion.

To solve the matching problems caused by the large intensity difference between the multi-source images and the nonlinear radiation distortion, we present a multi-source image matching approach that considers the orientation of the phase sharpness. First, the scale-space of the image pyramid was constructed, and the phase consistency of the image frequency domain was solved to obtain the maximum moment feature, and the KAZE operator is used to extract the feature points. Next, the Log-Gabor even symmetric filter was used to perform Fourier transform, and the improved local phase sharpness feature and phase orientation feature were constructed respectively to replace the gradient amplitude and gradient direction feature of the image. A descriptor of local phase sharpness orientation (LPSO) was established through the log-polar description template, and finally the Euclidean distance was used to measure the similarity to obtain the corresponding points. Multiple sets of typical multi-source images were used as data sources, and the LPSO algorithm was compared with SIFT, LGHD, RIFT and HAPCG algorithms in datasets. Evaluation of the algorithm performances indicates that the proposed method is more accurate and robust in the task of multi-source image matching.