A STUDY ON AUTOMATIC UAV IMAGE MOSAIC METHOD FOR PAROXYSMAL DISASTER
A STUDY ON AUTOMATIC UAV IMAGE MOSAIC METHOD FOR PAROXYSMAL DISASTER
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DOI：
10.5194/isprsarchives-xxxix-b6-123-2012
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发表时间：
2012-07
期刊：
影响因子：
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通讯作者：
Ming Li;Deren Li;Dengke Fan
中科院分区：
文献类型：
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作者：
Ming Li;Deren Li;Dengke Fan
众所周知，一些突发性的灾害，如洪水，能在短时间内造成巨大的损失。及时、准确、快速地获取充足的灾害信息是应对灾害应急的前提。由于无人机在获取灾害数据方面的优势，无人机这一新兴的遥感数据逐渐成为防灾减灾部门获取第一手灾害信息的首选。本文提出了一种新颖、快速的无人机数据配准和镶嵌策略。首先，在SIFT算子初始化过程中，原始图像不会被放大到2倍大，并减少尺度空间中金字塔倍频程的总数，以加快匹配过程;然后，使用RANSAC（Random Sample Consensus）消除不匹配的连接点。然后，采用光束法平差对摄像机几何标定参数进行联合求解。最后，将基于动态调度的最佳接缝搜索策略应用于飞机侧视时的避障问题。此外，采用加权融合估计算法消除“融合鬼”现象。
As everyone knows, some paroxysmal disasters, such as flood, can do a great damage in short time. Timely, accurate, and fast acquisition of sufficient disaster information is the prerequisite facing with disaster emergency. Due to UAV's superiority in acquiring disaster data, UAV, a rising remote sensed data has gradually become the first choice for departments of disaster prevention and mitigation to collect the disaster information at first hand. In this paper, a novel and fast strategy is proposed for registering and mosaicing UAV data. Firstly, the original images will not be zoomed in to be 2 times larger ones at the initial course of SIFT operator, and the total number of the pyramid octaves in scale space is reduced to speed up the matching process; sequentially, RANSAC(Random Sample Consensus) is used to eliminate the mismatching tie points. Then, bundle adjustment is introduced to solve all of the camera geometrical calibration parameters jointly. Finally, the best seamline searching strategy based on dynamic schedule is applied to solve the dodging problem arose by aeroplane's side-looking. Beside, a weighted fusion estimation algorithm is employed to eliminate the "fusion ghost" phenomenon.