Registration for Optical Multimodal Remote Sensing Images Based on FAST Detection, Window Selection, and Histogram Specification

Remote Sensing. 2018;10(5):663 DOI 10.3390/rs10050663

 

Journal Homepage

Journal Title: Remote Sensing

ISSN: 2072-4292 (Print)

Publisher: MDPI AG

LCC Subject Category: Science

Country of publisher: Switzerland

Language of fulltext: English

Full-text formats available: PDF, HTML

 

AUTHORS

Xiaoyang Zhao (College of Resource and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan 430070, China)
Jian Zhang (College of Resource and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan 430070, China)
Chenghai Yang (USDA-Agricultural Research Service, Aerial Application Technology Research Unit, 3103 F & B Road, College Station, TX 77845, USA)
Huaibo Song (College of Mechanical and Electronic Engineering, Northwest A&F University, 22 Xinong Road, Yangling 712100, China)
Yeyin Shi (Department of Biological Systems Engineering, University of Nebraska-Lincoln, 3605 Fair Street, Lincoln, NE 68583, USA)
Xingen Zhou (Texas A&M AgriLife Research and Extension Center, Beaumont, TX 77713, USA)
Dongyan Zhang (Anhui Engineering Laboratory of Agro-Ecological Big Data, Anhui University, Hefei 230601, China)
Guozhong Zhang (College of Engineering, Huazhong Agricultural University, 1 Shizishan Street, Wuhan 430070, China)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 11 weeks

 

Abstract | Full Text

In recent years, digital frame cameras have been increasingly used for remote sensing applications. However, it is always a challenge to align or register images captured with different cameras or different imaging sensor units. In this research, a novel registration method was proposed. Coarse registration was first applied to approximately align the sensed and reference images. Window selection was then used to reduce the search space and a histogram specification was applied to optimize the grayscale similarity between the images. After comparisons with other commonly-used detectors, the fast corner detector, FAST (Features from Accelerated Segment Test), was selected to extract the feature points. The matching point pairs were then detected between the images, the outliers were eliminated, and geometric transformation was performed. The appropriate window size was searched and set to one-tenth of the image width. The images that were acquired by a two-camera system, a camera with five imaging sensors, and a camera with replaceable filters mounted on a manned aircraft, an unmanned aerial vehicle, and a ground-based platform, respectively, were used to evaluate the performance of the proposed method. The image analysis results showed that, through the appropriate window selection and histogram specification, the number of correctly matched point pairs had increased by 11.30 times, and that the correct matching rate had increased by 36%, compared with the results based on FAST alone. The root mean square error (RMSE) in the x and y directions was generally within 0.5 pixels. In comparison with the binary robust invariant scalable keypoints (BRISK), curvature scale space (CSS), Harris, speed up robust features (SURF), and commercial software ERDAS and ENVI, this method resulted in larger numbers of correct matching pairs and smaller, more consistent RMSE. Furthermore, it was not necessary to choose any tie control points manually before registration. The results from this study indicate that the proposed method can be effective for registering optical multimodal remote sensing images that have been captured with different imaging sensors.