Automatic registration of remote sensing images is an essential task that requires the establishment of appropriate correspondences between the sensed image and the reference image. Among the feature-based matching approaches, attempts relied on the scale-invariant feature transform (SIFT) algorithm have shown particular superiority over other methods. From the perspective of automatic registration, the challenges of SIFT-based methods involve the elimination of mismatches and the homogeneity of matches. While many outlier removal methods have been proposed, the strategies for uniform distribution have been lacking. To address these two issues, this article investigates a new remote sensing image registration algorithm based upon a revised SIFT scheme. Additionally, an outlier removal mechanism founded on a trilateral computation (Tc) recipe and a homogeneity enforcement (He) layout according to a divide-and-conquer inclusion tactic are proposed. Finally, a stochastic competition process based upon game theory is introduced to secure an appropriate amount of correct matches in the proposed Tc and He (TcHe)-SIFT framework. A wide variety of multispectral and multitemporal remote sensing images with various scenarios were exploited to evaluate the proposed registration algorithm. Experimental results demonstrated the advantages of our developed remote sensing image registration algorithm over the state-of-the-art SIFT-based methods. We believe that this new registration technique is of potential in a number of remote sensing image processing applications.

中文翻译：

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基于修正SIFT三边计算和均匀性执行的遥感影像自动配准


遥感图像的自动配准是一项重要任务，需要在感测图像和参考图像之间建立适当的对应关系。在基于特征的匹配方法中，依赖于尺度不变特征变换（SIFT）算法的尝试已经显示出比其他方法特别的优越性。从自动配准的角度来看，基于SIFT的方法面临的挑战包括消除不匹配和匹配的同质性。虽然已经提出了许多异常值去除方法，但缺乏均匀分布的策略。为了解决这两个问题，本文研究了一种基于改进的 SIFT 方案的新遥感图像配准算法。此外，还提出了一种基于三边计算（Tc）配方的异常值去除机制和根据分而治之包含策略的同质性执行（He）布局。最后，引入基于博弈论的随机竞争过程，以确保所提出的 Tc 和 He (TcHe)-SIFT 框架中适当数量的正确匹配。利用具有各种场景的各种多光谱和多时相遥感图像来评估所提出的配准算法。实验结果证明了我们开发的遥感图像配准算法相对于最先进的基于 SIFT 的方法的优势。我们相信这种新的配准技术在许多遥感图像处理应用中具有潜力。