本文關(guān)鍵詞： 合成孔徑雷達(dá) 圖像配準(zhǔn) 特征向量 尺度不變特征變換　出處：《系統(tǒng)工程與電子技術(shù)》2017年05期 　論文類(lèi)型：期刊論文

【摘要】：在基于尺度不變特征變換算法的合成孔徑雷達(dá)圖像配準(zhǔn)算法中,一個(gè)特征點(diǎn)通常具有多個(gè)主方向,雖然該主方向分配方式可以有效增加正確匹配對(duì)數(shù),但是匹配性能會(huì)受到特征向量之間的相互影響而下降。文章提出了一種最佳歐式距離匹配方法,該方法通過(guò)歐式空間距離矩陣計(jì)算待匹配圖像兩組特征向量集的相似度,獲得最佳相似特征點(diǎn)。此外,文章引入代表位置關(guān)系的轉(zhuǎn)換距離作為判斷特征點(diǎn)空間一致性的依據(jù),有效地消除錯(cuò)誤匹配點(diǎn)。與DM等匹配方法相比較,最佳歐式空間距離矩陣匹配方法在匹配精度和匹配效率上驗(yàn)證了其有效性。
[Abstract]:In the synthetic Aperture Radar (SAR) image registration algorithm based on scale-invariant feature transformation, a feature point usually has multiple principal directions, although the allocation of the principal direction can effectively increase the correct matching logarithm. However, the matching performance will be affected by the interaction of the feature vectors. In this paper, an optimal Euclidean distance matching method is proposed, which calculates the similarity between the two groups of feature vectors in the image by Euclidean space distance matrix. The optimal similarity feature points are obtained. In addition, the transformation distance representing the location relationship is introduced as the basis for judging the spatial consistency of feature points, and the error matching points are effectively eliminated, which is compared with DM and other matching methods. The best Euclidean space distance matrix matching method verifies its validity in terms of matching accuracy and matching efficiency.
【作者單位】： 西北工業(yè)大學(xué)電子信息學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(61401363) 航空科學(xué)基金(20155153034)資助課題
【分類(lèi)號(hào)】：TN957.52
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本文編號(hào)：1533874