研發一精準特徵點配對演算法從事自動遙測衛星影像套合

An Accurate Feature Point Matching Algorithm for Automatic Remote Sensing Image Registration

吳冠龍(Guan-Long Wu)

指導教授 ： 張恆華

國立臺灣大學/工學院/工程科學及海洋工程學研究所/碩士(2017年)

https://doi.org/10.6342/NTU201703317

摘要

影像對位是將兩個相同物件但是不同來源的影像對齊的過程。因為衛星影像有不同的影像型態和複雜的影像模型，所以衛星影像對位仍然是一個具有挑戰性的研究。本論文提出一個強健和精確的方法來實現自動衛星影像對位。我們提出的方法分成四大步驟，第一步驟是藉由改良的尺度不變特徵轉換的方法，透過雙線性內插法的重採樣可以更精確地找到特徵點位置，然後將兩組特徵點進行配對。第二步驟是進行樣本特徵點配對(sample feature point matching)，再計算樣本特徵點(sample feature point)的斜率，然後使用k-平均演算法對樣本特徵點的斜率進行分群。一方面，每一群的邊界可以得到標準配對邊界(standard grouping boundary)，另一方面，藉由每一群的特徵點位置可以計算出自己的自信橢圓邊界(confidence elliptical boundary)。第三步驟為尺度不變特徵轉換在距離比值為0.9進行的配對結果，先透過標準配對邊界對特徵配對點的斜率值進行分群，如果該群的配對特徵點位置在自信橢圓邊界之外將會被認定是錯誤的配對且將其配對刪去。第四步驟使用隨機抽樣一致算法(RANSAC)，在篩選後的特徵配對之中，找到最適合的轉換方程式。我們將此方法與其他衛星影像對位的方法比較之下可以得到更精確的結果，並且應用在實際的城市、河流等多時影像，可藉由此方法觀察城市發展和河流侵蝕堆積的變化。

關鍵字

遙測影像 ； 影像套合 ； 尺度不變特徵轉換 ； 隨機抽樣一致算法

並列摘要

Remote sensing image registration is still a challenging task because of the variety in image types and the lack of a consistent transformation. To improve image registration for remote sensing, a robust and accurate method is developed in this thesis. To begin with, a modified scale-invariant feature transform (SIFT) method is proposed for feature point detection and pair matching. Based on the properties of matched pairs, the standard grouping boundary (SGB) and confidence elliptical boundary (CEB) are computed for further examination. The SGB is used to categorize matched pairs according to the slope values. The CEB is a geometric contour in spatial scale to remove outliers if the key-point locations are outside the contour. At last, random sample consensus approach is implemented to find the most appropriate transformation function. The developed algorithm is tested on multi-temporal remote sensing images to show the variation in different timescales. The experimental results show the improvement of matching performance, the accuracy, and robustness of proposed method.

並列關鍵字

remote sensing image ； image registration ； SIFT ； RANSAC

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