Physics Maths Engineering
An Optical Remote Sensing Image Matching Method Based on the Simple and Stable Feature Database
Zilu Zhao,
Hui Long,
Hongjian You
Hongjian You
Aerospace Information Research Institute, Chinese Academy of Sciences
Abstract
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.
Key Questions
What challenge does the study address?
The study addresses the challenge of efficiently and accurately matching optical remote sensing images in the era of big data, where the increasing volume of satellite imagery demands effective processing methods.
What solution does the study propose?
The study proposes an optical remote sensing image matching method based on a simple stable feature database. This method involves building a stable feature database by extracting local invariant features from remote sensing images using an iterative matching strategy and storing useful information about these features.
How does this method improve image matching?
By utilizing a feature database-based matching approach without reference images, the method potentially saves storage space and increases image processing speed. In comparison with direct matching using classical feature algorithms, this approach can improve the correct rate of feature point matching by more than 30% and reduce the matching time by more than 40%.
What datasets were used to evaluate the method?
The method was evaluated using various local invariant feature algorithms on different time phases of Gaofen-2 (GF-2) images. Matching comparison experiments with various satellite images were also conducted to confirm the viability and stability of the feature database-based matching method.
What are the potential benefits of this method?
This method improves the accuracy and timeliness of image matching, potentially solves the problem of large storage space occupied by reference data, and has great potential for fast matching of optical remote sensing images.
