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

Xiaoyang Zhao,Guozhong Zhang,Yeyin Shi,Xingen Zhou,Jian Zhang,Chenghai Yang,Huaibo Song,Dongyan Zhang

doi:10.3390/rs10050663

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.

Highlights

A novel method was proposed for the registration of optical multimodal remote sensing images, based on from accelerated segment test (FAST) detection, window selection, and histogram specification
The image analysis showed that the FAST detector with a rapid processing speed was suitable for extracting feature points for subsequent point matching
Since the window selection reduced the search space and the histogram specification optimized the feature similarity, the combination of these two techniques made the number of correctly matched point pairs increase by 11.30 times and the correct matching rate increase by 36%, compared with the results based on FAST alone

Summary

Introduction

Imasgpeecriefigciasttiorantion is an important image pre-processing procedure [1] that is required to align the images that are captured with different imaging sensors in remote sensing. Image registration involves the alignment of two or more images from optical imaging camera1s.oInrtirmodaugcetiodnata from other sources, such as digital elevation models [2], captured at different times and from different viewpoints [3] or by different sensors [4]. Temporal imImaaggeesrceoguistlrdatbioenuisseadn ifmoproarttainmt eimsaegreiepsrea-nparolycessissin[g5]p,raoncedduimrea[g1]etshafrtoismredquififreerdetnotavliigenwpoints the images that are captured with different imaging sensors in remote sensing. Aorlmthoorue igmhagsoesmfreomreomptoictealsensing sensorsimcaagnincgacpatmuerreasmoruilmtiasgpeedctartaalfriommaogtheesrwsoiuthrcoesu,tsutchheans edeigditafloerleivmataiognemaoldigenlsm[2e],ncta,ptmuroesdtaat irborne multispdeicffterraelnitmtaimgiensgasnydstfermoms cdaipffteurernetmvuielwtipspoiencttsra[3l]imoragbeys dwififtehremntuslteinpsloersca[m4].erTahsrourgimh aimgiangge sensors that reqruegirisetriamtiaong,et-etmo-pimoraalgiemagliegsncmoueldntb.e used for a time series analysis [5], and images from different. Some commonly-used Almthuolutisgphecitmraal igminagginsgyssytestmemssththaatt aarreebbaasesdedonodnigditiagliftraalmferacmameercaasmareersahsorwenquinirFeigiumrea1g.e registration and radiomAeltrhiocugcahlimbraagtiinogns,ytshteemys hthaavtearme baansyedaodnvdaingittaagl efrsamfoercaremmeroasteresqeunirseinimg,agine crelugidstirnagtiotnheir low cost, smaandll rsaidzieo,maentrdicecaasliebroaftiouns,eth[e1y2]h.aUvenmliakneysaodmvaentsacgieesnftoirfircemmouteltsiesnpseincgtr, ainlcolurdhinygptehresirpleocwtrcaolstc,ameras, which asrme ablal ssiezde,oanndtheaesleinofeuasrer[a1y2]s. eUnnsloikres,sowmheicshciednotifnicomt nuelteisdpeacltirganl morehnytp,ecrospmemctroanl claym-uesreads, wmhuiclthispectral cameraacsarewmbeitarhassefdwraoitmnh etfhrasemelniensseoeransrsrorareysqrsueeqniruseoirraesl,alwlol hofifcthhtheedsosppneeocctttrrnaalelebdbanaadnlisgdtnsomtboeenbat,leicgoanmleidgmntooendolynt-euosaoendnotemheaurnl.toAistpsheaecltrlr.oaAfl s all of the bantdhse bhaanvdes dhaifvfeerdeifnfetrsepnet csptreacltrraal nragnegse,si,titisisssoommeettiimmesesdidffiifcfiucltutlot itdoenidtiefynctoifmymcoonmfmeaotunrefepaotinutrse points among atmheonbganthde bimanadgiemsa, geessp, eescpiaelcliyallbyebtwetweeenentthhee vviissiibblleeaannddNNIRIbRanbdasn. ds

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Methods

Results

Conclusion