UAV (unmanned aerial vehicle) remote sensing system has advantages of strong real-time, flexible and convenient, little influence by the external environment, and the ability to work full-time. It can go deep into the places safely and reliably which staff can hardly arrived. The remote sensing system can be in response to emergencies to gain first-hand information as quickly as possible and have produced a unique emergency response to acquire an important basis for overall decision-making. However, UAV remote sensing system was so fast, flexible, low flying to carry on quick response to acquire high-resolution images. In the Wenchuan Earthquake, UAV remote sensing was applied successfully to acquire first-hand earthquake damage information in the short time under cloudy and rainy conditions of Sichuan Province. The system flow of UAV remote sensing to extract information on damaged houses after earthquake was set up successfully. Moreover, UAV remote sensing had an important role in mapping of damaged buildings after earthquake. Rapid identification of mapping of damaged buildings after earthquake with UAV remote sensing techniques can be carried out. UAV remote sensing techniques could have greater potentials for disaster mitigation and management after earthquake.
Unmanned Aerial Vehicle Remote Sensing (UAVRS) have developed rapidly driven mainly for military reconnaissance, earth observation and scientific data collection between military and civilian users over the past decade. However, automatic registration of UAVRS images has become a problem of blocks for the wide applications. In this paper, an algorithm based on both Random Sample Consensus (RANSAC) and least-squares method is proposed to improve the image registration performance of SIFT algorithm. On the one hand, RANSAC can remove inaccurate feature point pairs that SIFT detected. On the other hand, given all rough feature matches based on SIFT features, least-squares match is used to carry out precise matching. The experiment results show that our proposal can effectively estimate matching error with an average correct matching rate of 92.8%. And also the new algorithm had faster matching rate for the same number of images under the same experimental platform. As a result, the algorithm can improve greatly the accuracy of matching, but also to reduce the computation load based on the experiment results. Automatic registration of UAVRS images can be obtained in real time. After pre-matching by SIFT feature matching algorithm, the least squares matching is used to match accurately, which can be satisfied for the relative orientation of low-altitude remote sensing images automatically.