Satellite remote sensing has entered the era of big data due to the increase in the number of remote sensing satellites and imaging modes. This presents significant challenges for the processing of remote sensing systems and will result in extremely high real-time data processing requirements. The effective and reliable geometric positioning of remote sensing images is the foundation of remote sensing applications. In this paper, we propose an optical remote sensing image matching method based on a simple stable feature database. This method entails building the stable feature database, extracting local invariant features that are comparatively stable from remote sensing images using an iterative matching strategy, and storing useful information about the features. Without reference images, the feature database-based matching approach potentially saves storage space for reference data while increasing image processing speed. To evaluate the performance of the feature database matching method, we train the feature database with various local invariant feature algorithms on different time phases of Gaofen-2 (GF-2) images. Furthermore, we carried out matching comparison experiments with various satellite images to confirm the viability and stability of the feature database-based matching method. In comparison with direct matching using the classical feature algorithm, the feature database-based matching method in this paper can essentially improve the correct rate of feature point matching by more than $30\%$ and reduce the matching time by more than $40\%$. This method improves the accuracy and timeliness of image matching, potentially solves the problem of large storage space occupied by the reference data, and has great potential for fast matching of optical remote sensing images.

The surge in remote sensing satellites and diverse imaging modes poses substantial challenges for ground systems. Swift and high-precision geolocation is the foundational requirement for subsequent remote sensing image applications. Breakthroughs in intelligent on-orbit processing now enable on-orbit geometric processing. In the absence of control data on board, a recent trend is to introduce reference data onto satellites. However, the pre-storage of traditional reference images or control point databases presents a significant challenge to the limited on-board data storage capacity. Therefore, oriented to the demand for control information acquisition during on-orbit geometry processing, we propose the construction of lightweight and stable feature databases. Initially, stable feature classes are obtained through iterative matching filtering, followed by re-extracting feature descriptors for each stable feature point location on the training images. Subsequently, the descriptors of each point location are clustered and fused using affinity propagation (AP) to eliminate redundancy. Finally, LDAHash is utilized to quantize floating-point descriptors into binary descriptors, further reducing the storage space. In our experiments, we utilize a variety of feature algorithms to assess the generality of our proposed method, thus extending the scope of the feature database and its applicability to various scenarios. This work plays a crucial role in advancing the technology of on-orbit geometry processing for remote sensing satellites.

遥感图像配准作为遥感图像处理中的基础性与关键性任务，始终是国内外学者的研究热点，具有重要的理论意义与应用价值。然而，遥感图像常面临显著几何畸变、非线性辐射差异、复杂地物遮挡及噪声干扰等挑战，制约了其高精度鲁棒配准的实现。本文系统梳理了遥感图像配准算法：首先，概述配准的基本概念与技术框架；其次，将现有方法划分为两类进行归纳梳理——基于知识驱动专家设计的方法与基于数据驱动深度学习的方法。前者依托手工设计特征及变换模型，通过显式特征提取与迭代优化实现传统场景下的可解释性配准，典型代表为尺度不变特征变换及其改进算法；后者利用深度学习网络自动学习图像对间的特征或映射关系，提取高级特征用于配准或端到端预测变换模型，有效应对显著几何畸变、非线性辐射差异等复杂场景，典型方法包括基于卷积神经网络的特征匹配网络及其改进网络。最后，总结现有方法的局限性，并展望遥感图像配准领域的未来发展趋势与面临的关键挑战。