Yao Guoqing

D-InSAR Technique for Land Subsidence Monitoring

Abstract The land subsidence has been a global disastrous problem, and the conventional geodetic technique is highly incompetent in the large-scale and serious land deformation monitoring. The D-InSAR technique has been widely applied and deeply researched in the field of the land deformation monitoring. This article first puts forward one of the methods known as time series radar interferometry based on permanent scatterers technique, and then, applies the time series analysis method into D-InSAR. This paper takes the phase difference of two nearer permanent scatterers in an interferogram as research object; therefore, the atmospheric delay impact can be eliminated. In the research, there are several steps including preprocessing radar images, selecting the PS points, combination of PS points, computing the variance of the phase difference for two PS points, integer least-squares adjustment, obtaining land subsidence velocity and so forth. The land subsidence data and the distribution map obtained in this paper by adapting this method to the experiment of Tianjin area subsidence monitoring have been proved to be satisfactory. The method for eliminating the atmospheric delay impact accurately has been realized by combining the time seriesmethod with PS technique. At the same time, it was confirmed that the time series for InSAR is a feasible and effective method for monitoring land deformation. Moreover, it can be concluded from the result obtained in the paper that the selection of PS points is so important that it is worthy studying further on.

Aircraft Detection of High-Resolution Remote Sensing Image Based on Faster R-CNN Model and SSD Model

With the continuous improvement of the space resolution in remote sensing images, the rapid and accurate detection in high-resolution remote sensing images has become a hotspot in the field of remote sensing application. For nearly 10 years, deep learning has made outstanding achievements in the feature extraction of original image and received attention of a large number of scholars. Among them, the convolutional neural network (CNN) has made breakthrough progress in the field of image classification and detection, and has overcome three shortcomings of the original remote sensing image detection method: low detection efficiency, redundant human resource input, and flawed feature selection. In this paper, Faster R-CNN model and SSD model are trained by high-resolution remote sensing images. The appropriate training time is determined by the detection results of verification set and the loss function. When we get trained models, it will be used to detect the test set images, and the accuracy rate and recall rate of two models were calculated by visual interpretation method. The experimental results show that both the Faster R-CNN model and the SSD model can be applied to aircraft detection in corresponding high-resolution remote sensing images. The SSD model can detect the single scene aircraft quickly and accurately. The Faster R-CNN model has a high accuracy but cannot reach the requirement of real-time detection. Besides, the accuracy rate and recall rate of Faster R-CNN model was significantly higher than the SSD model in the complex scenes, and the Faster R-CNN model has a great advantage for the detection of small aircraft.

Joint Statistics on Stereographic Projection of Contour Creating Algorithm Based on Regular Grid Data

A joint is the structural trace generated by a certain part of crust by the action of tectonic stress in some epoch. The development law and characteristic of it extremely correlate with relevant structure in its area. Joint statistics plays an important role in structural analysis and engineering geological evaluation. Due to extensive data in actual measurement, heavy workload in traditional manual mapping and limited mapping precision of Wulff Net used in manual mapping process, manual mapping lacks precision and results in many errors. In recent years, there are a few researches involving realizing the function of storegraphic projection Joint statistics with the help of computers. We propose a method to realize the joint statistics on stereographic projection of contour creating algorithm based on regular grid data, through using statistical law of Pronin Net and inverse distance weighted grid interpolation algorithm, smooth handling of contour line by adopting B-Spline, contour line filled on the basis of topological relationship, so that stereographic projection net, joint point diagram, joint iso-intensity diagram and the filling of joint iso-intensity diagram can finally be drawn, and the accuracy of stereographic projection and preciseness of joint statistics can be improved dramatically.