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An Efficient and Precise Remote Sensing Optical Image Matching Technique Using Binary-Based Feature Points.

Min-Lung Cheng¹, Masashi Matsuoka¹

¹Department of Architecture and Building Engineering, School of Environment and Society, Tokyo Institute of Technology, Yokohama 226-8502, Japan.

Sensors (Basel, Switzerland)

|September 28, 2021

Summary

This study introduces a new algorithm for optical image matching, improving speed and precision. The synthetic-colored enhanced accelerated binary robust invariant scalar keypoints (SC-EABRISK) method with affine transformation with bounding box (ATBB) significantly enhances feature point matching.

Keywords:

BRISK color space geometric mapping image matching

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Area of Science:

Computer Vision
Image Processing
Geometric Algorithms

Background:

Local feature point matching is critical for image registration, mosaicking, and structure-from-motion.
Existing methods face challenges in feature robustness, match quantity, and processing efficiency.

Purpose of the Study:

To develop a systematic algorithm addressing robustness, match quantity, and efficiency in image feature matching.
To enhance image matching precision and speed for optical image processing applications.

Main Methods:

Integration of synthetic-colored enhanced accelerated binary robust invariant scalar keypoints (SC-EABRISK) for descriptor enhancement with color information.
Application of the affine transformation with bounding box (ATBB) procedure for geometric mapping to recover additional feature matches.

Main Results:

The developed SC-EABRISK with ATBB algorithm demonstrates up to 20x speed improvement over the previous EABRISK method.
Achieved thousands of feature matches, significantly increasing the quantity of successful correspondences.
Improved matching precision by over 90% across benchmark, close-range, aerial, and satellite image datasets.

Conclusions:

The SC-EABRISK with ATBB algorithm provides an efficient and precise solution for local feature point matching.
The method effectively addresses key challenges in optical image processing, enhancing overall performance.