【摘要】：在機載高光譜遙感數(shù)據(jù)預(yù)處理中,高光譜圖像幾何校正始終是不可或缺的重要部分。依據(jù)傳統(tǒng)算法,計算中必然性的需要巨大搜索量或者其他大量計算,要花費大量時間,而本文便是針對此因素對其算法進行優(yōu)化,以達到大幅度提高數(shù)據(jù)處理速度,節(jié)約時間的目的。 Applanix公司的POS/AV位置姿態(tài)系統(tǒng)是用于機載傳感器的POS系統(tǒng),位置精度可達5cm-30cm,方向精度可以精確到二十秒至三十秒之間,從而為數(shù)據(jù)處理提供相對高精度的位置和姿態(tài)數(shù)據(jù)。本文便是充分利用高精度匹配位置姿態(tài)數(shù)據(jù),如經(jīng)緯度、海拔、側(cè)滾角和俯仰角等參數(shù),使用幾何校正算法重采樣每個像元的地面實際坐標(biāo)和灰度值,從而消除遙感平臺帶來的幾何畸變。 本文首先依次介紹了高光譜遙感的特點與應(yīng)用、幾何畸變原因、幾何校正并行的需求,其中對幾何畸變影響最大的便是機載平臺的不穩(wěn)定,所以取得高精度的位置姿態(tài)數(shù)據(jù)尤為重要。在第二章中便詳細介紹了POS系統(tǒng)四個組成部分的工作原理,特別闡述了GPS/IMU組合后能得到高精度位置姿態(tài)數(shù)據(jù)的緣由。同時在幾何校正之前,針對高光譜圖像中出現(xiàn)的圖像行缺失、行重復(fù)誤差,經(jīng)Matlab仿真分析后,得出了行號值亂碼規(guī)律,而且分析出此內(nèi)部畸變原因除傳感器誤差之外,行光譜信息傳輸限制也是一大因素,隨之提出了可行方案,并實現(xiàn)了圖像的復(fù)原,復(fù)原后整體效果良好。然后對幾何校正算法中坐標(biāo)變換和重采樣做了詳細介紹。針對傳統(tǒng)重采樣方法,本文提出了新的重采樣算法即局部搜索距離倒數(shù)法,此算法不需要大量搜索且重采樣效率較高,經(jīng)此算法處理后,圖像校正后整體效果良好。最后針對高光譜遙感數(shù)據(jù)以波段排列的特性和單機多核的特點,提出了多線程并行方案。在坐標(biāo)變換過程中,數(shù)據(jù)讀寫始終頻繁,而數(shù)據(jù)讀寫是單線程進行,所以對坐標(biāo)變換部分進行數(shù)據(jù)并行是不可行的,而重采樣部分在計算中以波段、行數(shù)和列數(shù)為迭代次數(shù)進行計算,所以重采樣適合多線程并行。在并行計算中,基于負載均衡提出了內(nèi)核動態(tài)綁定模式的并行模式,然后對其重采樣效率的提高做了詳細的分析。最后結(jié)果表明,多線程內(nèi)核綁定模式不僅大幅度提高了重采樣效率,而且也大大減少了因負載均衡而帶來的內(nèi)存開銷。 隨著高光譜圖像分辨率的提高和多核技術(shù)的飛速發(fā)展,如何充分利用高性能計算對提高遙感圖像幾何校正重采樣效率有著一定的研究意義。
[Abstract]:Geometric correction of hyperspectral images is always an important part in airborne hyperspectral remote sensing data preprocessing. According to the traditional algorithm, the necessity of computing needs a large amount of search or other large amount of computation, and it takes a lot of time, and this paper optimizes the algorithm to achieve a significant increase in the speed of data processing. Applanix's POS/AV position and attitude system is a POS system for airborne sensors, with a position accuracy of 5 cm to 30 cm and a directional accuracy of between 20 seconds and 30 seconds. Thus, the position and attitude data are relatively high precision for data processing. In this paper, high precision matching position and attitude data, such as longitude and latitude, altitude, roll angle and pitch angle, are fully used to resample the actual ground coordinates and gray values of each pixel by using geometric correction algorithm. Thus the geometric distortion brought by remote sensing platform is eliminated. In this paper, the characteristics and applications of hyperspectral remote sensing, the causes of geometric distortion and the requirement of parallel geometric correction are introduced in turn. The instability of airborne platform is the most important influence on geometric distortion. So it is very important to obtain the position and attitude data with high accuracy. In the second chapter, the working principle of the four components of POS system is introduced in detail, especially the reason why the high precision position and attitude data can be obtained by GPS/IMU combination. At the same time, before geometric correction, aiming at the line missing and line repeating error in hyperspectral image, after Matlab simulation analysis, the law of line number disorder code is obtained, and the reason of this internal distortion is analyzed besides sensor error. The limitation of line spectral information transmission is also a major factor. A feasible scheme is proposed and the image restoration is realized. The overall effect of the restoration is good. Then, coordinate transformation and resampling in geometric correction algorithm are introduced in detail. For the traditional resampling method, a new resampling algorithm, the local search distance reciprocal method, is proposed in this paper. The algorithm does not require a lot of search and the resampling efficiency is high. After the algorithm is processed, the overall effect of image correction is good. Finally, according to the characteristics of hyperspectral remote sensing data arranged in band and single machine multi-core, a multi-thread parallel scheme is proposed. In the process of coordinate transformation, data reading and writing is always frequent, and data reading and writing is carried out by a single thread, so it is not feasible to parallelize the coordinate transformation part, while the resampling part takes the band in the calculation. The number of rows and columns is calculated by iterations, so resampling is suitable for multithread parallelism. In parallel computing, the parallel mode of kernel dynamic binding mode is proposed based on load balancing, and the improvement of resampling efficiency is analyzed in detail. The results show that the multi-thread kernel binding mode not only improves the efficiency of resampling greatly, but also reduces the memory overhead caused by load balancing. With the improvement of the resolution of hyperspectral images and the rapid development of multi-core technology, how to make full use of high performance computing has a certain significance in improving the efficiency of geometric correction resampling of remote sensing images.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP751

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