Next Article in Journal
Satellite Images and Gaussian Parameterization for an Extensive Analysis of Urban Heat Islands in Thailand
Previous Article in Journal
ASTER-Derived High-Resolution Ice Surface Temperature for the Arctic Coast
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(5), 663; https://doi.org/10.3390/rs10050663

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

1
College of Resource and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan 430070, China
2
Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtse River), Ministry of Agriculture, 1 Shizishan Street, Wuhan 430070, China
3
USDA-Agricultural Research Service, Aerial Application Technology Research Unit, 3103 F & B Road, College Station, TX 77845, USA
4
College of Mechanical and Electronic Engineering, Northwest A&F University, 22 Xinong Road, Yangling 712100, China
5
Department of Biological Systems Engineering, University of Nebraska-Lincoln, 3605 Fair Street, Lincoln, NE 68583, USA
6
Texas A&M AgriLife Research and Extension Center, Beaumont, TX 77713, USA
7
Anhui Engineering Laboratory of Agro-Ecological Big Data, Anhui University, Hefei 230601, China
8
College of Engineering, Huazhong Agricultural University, 1 Shizishan Street, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Received: 23 March 2018 / Revised: 16 April 2018 / Accepted: 21 April 2018 / Published: 24 April 2018
(This article belongs to the Section Remote Sensing Image Processing)
Full-Text   |   PDF [14204 KB, uploaded 3 May 2018]   |  

Abstract

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. View Full-Text
Keywords: optical multimodal images; registration; FAST; window selection; histogram specification optical multimodal images; registration; FAST; window selection; histogram specification
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zhao, X.; Zhang, J.; Yang, C.; Song, H.; Shi, Y.; Zhou, X.; Zhang, D.; Zhang, G. Registration for Optical Multimodal Remote Sensing Images Based on FAST Detection, Window Selection, and Histogram Specification. Remote Sens. 2018, 10, 663.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top