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Satellite Image Matching and Registration: A Comparative Study Using Invariant Local Features

Tahoun, Mohamed and Shabayek, Abd El Rahman and Nassar, Hamed and Giovenco, Marcello and Reulke, Ralf and Emary, Eid and Hassanien, Aboul Ella (2016) Satellite Image Matching and Registration: A Comparative Study Using Invariant Local Features. In: Image Feature Detectors and Descriptors: Foundations and Applications Springer. pp. 135-171. ISBN 3319288547, 9783319288543.

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Abstract

The rapid increasing of remote sensing (RS) data in many applications ignites a spark of interest in the process of satellite image matching and registration. These data are collected through remote sensors then processed and interpreted by means of image processing algorithms. They are taken from different sensors, viewpoints, or times for many industrial and governmental applications covering agriculture, forestry, urban and regional planning, geology, water resources, and others. In this chapter, a feature-based registration of optical and radar images from same and different sensors using invariant local features is presented. The registration process starts with the feature extraction and matching stages which are considered as key issues when processing remote sensing data from single or multi-sensors. Then, the geometric transformation models are applied followed by the interpolation method in order to get a final registered version. As a pre-processing step, speckle noise removal is performed on radar images in order to reduce the number of false detections. In a similar fashion, optical images are also processed by sharpening and enhancing edges in order to get more accurate detections. Different blob, corner and scale based feature detectors are tested on both optical and radar images. The list of tested detectors includes: SIFT, SURF, FAST, MSER, Harris, GFTT, ORB, BRISK and Star. In this work, five of these detectors compute their own descriptors (SIFT, SURF, ORB, BRISK, and BRIEF), while others use the steps involved in SIFT descriptor to compute the feature vectors describing the detected keypoints. A filtering process is proposed in order to control the number of extracted keypoints from high resolution satellite images for a real time processing. In this step, the keypoints or the ground control points (GCPs) are sorted according to the response strength measured based on their cornerness. A threshold value is chosen to control the extracted keypoints and finalize the extraction phase. Then, the pairwise matches between the input images are calculated by matching the corresponding feature vectors. Once the list of tie points is calculated, a full registration process is followed by applying different geometric transformations to perform the warping phase. Finally and once the transformation model estimation is done, it is followed by blending and compositing the registered version. The results included in this chapter showed a good performance for invariant local feature detectors. For example, SIFT, SURF, Harris, FAST and GFTT achieve better performance on optical images while SIFT gives also better results on radar images which suffer from speckle noise. Furthermore, through measuring the inliers ratios, repeatability, and robustness against noise, variety of comparisons have been done using different local feature detectors and descriptors in addition to evaluating the whole registration process. The tested optical and radar images are from RapidEye, Pléiades, TET-1, ASTER, IKONOS-2, and TerraSAR-X satellite sensors in different spatial resolutions, covering some areas in Australia, Egypt, and Germany.

Item URL in elib:https://elib.dlr.de/116868/
Document Type:Contribution to a Collection
Title:Satellite Image Matching and Registration: A Comparative Study Using Invariant Local Features
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Tahoun, MohamedDepartment of Computer Science, FCI, Suez Canal University, Ismailia, EgyptUNSPECIFIED
Shabayek, Abd El RahmanDepartment of Computer Science, FCI, Suez Canal University, Ismailia, EgyptUNSPECIFIED
Nassar, HamedDepartment of Computer Science, FCI, Suez Canal University, Ismailia, EgyptUNSPECIFIED
Giovenco, MarcelloInstitut für Optische SensorsystemeUNSPECIFIED
Reulke, RalfInstitut für Optische SensorsystemeUNSPECIFIED
Emary, EidDepartment of Information Technology, FCI, Cairo university, Cairo, EgyptUNSPECIFIED
Hassanien, Aboul EllaDepartment of Information Technology, FCI, Cairo university, Cairo, EgyptUNSPECIFIED
Date:2016
Journal or Publication Title:Image Feature Detectors and Descriptors: Foundations and Applications
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 135-171
Editors:
EditorsEmail
Awad, Ali IsmailUNSPECIFIED
Hassaballah, MahmoudUNSPECIFIED
Publisher:Springer
ISBN:3319288547, 9783319288543
Status:Published
Keywords:Image matching and Registration, detector Features, optical and Radar images
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Optische Technologien und Anwendungen
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Optical Sensor Systems
Deposited By: Dombrowski, Ute
Deposited On:19 Dec 2017 09:57
Last Modified:19 Dec 2017 09:57

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