TY - JOUR

T1 - Comparison of Keypoint Detectors and Descriptors for Relative Radiometric Normalization of Bitemporal Remote Sensing Images

AU - Moghimi, Armin

AU - Celik, Turgay

AU - Mohammadzadeh, Ali

AU - Kusetogullari, Huseyin

N1 - Funding Information: Manuscript received February 8, 2021; revised March 20, 2021; accepted March 27, 2021. Date of publication March 31, 2021; date of current version April 21, 2021. This work was supported in part by Sichuan Provincial Science, and Technology Projects under Grant 2019JDJQ0023. (Corresponding author: Turgay Celik.) Armin Moghimi and Ali Mohammadzadeh are with the Department of Photogrammetry and Remote Sensing, Geomatics Engineering Faculty, K. N. Toosi University of Technology, Tehran 15433-19967, Iran (e-mail: moghimi.armin@gmail.com; a_mohammadzadeh@kntu.ac.ir). Publisher Copyright: © 2008-2012 IEEE.

PY - 2021/3/31

Y1 - 2021/3/31

N2 - This article compares the performances of the most commonly used keypoint detectors and descriptors (SIFT, SURF, KAZE, AKAZE, ORB, and BRISK) in keypoint-based relative radiometric normalization (RRN) of unregistered bitemporal multispectral images. The keypoints matched between subject and reference images represent possible unchanged regions and form a radiometric control set (RCS). The initial RCS is further refined by removing the matched keypoints with a low cross-correlation. The final RCS is used to approximate a linear mapping between the corresponding bands of the subject and reference images. This procedure is validated on five datasets of unregistered multispectral image pairs acquired by inter/intra sensors in terms of RRN accuracy, visual quality, quality, and quantity of the samples in the RCS, and computational time. The experimental results show that keypoint-based RRN is robust against variations in spatial-resolution, illumination, and sensors. The blob detectors (SURF, SIFT, KAZE, and AKAZE) are more accurate on average than the corner detectors (ORB and BRISK) in RRN, with an expense of higher computational cost. The source code and samples of datasets used in this study are made available at https://github.com/ArminMoghimi/keypoint-based-RRN to support reproducible research in remote sensing.

AB - This article compares the performances of the most commonly used keypoint detectors and descriptors (SIFT, SURF, KAZE, AKAZE, ORB, and BRISK) in keypoint-based relative radiometric normalization (RRN) of unregistered bitemporal multispectral images. The keypoints matched between subject and reference images represent possible unchanged regions and form a radiometric control set (RCS). The initial RCS is further refined by removing the matched keypoints with a low cross-correlation. The final RCS is used to approximate a linear mapping between the corresponding bands of the subject and reference images. This procedure is validated on five datasets of unregistered multispectral image pairs acquired by inter/intra sensors in terms of RRN accuracy, visual quality, quality, and quantity of the samples in the RCS, and computational time. The experimental results show that keypoint-based RRN is robust against variations in spatial-resolution, illumination, and sensors. The blob detectors (SURF, SIFT, KAZE, and AKAZE) are more accurate on average than the corner detectors (ORB and BRISK) in RRN, with an expense of higher computational cost. The source code and samples of datasets used in this study are made available at https://github.com/ArminMoghimi/keypoint-based-RRN to support reproducible research in remote sensing.

KW - AKAZE

KW - BRISK

KW - change detection

KW - KAZE

KW - keypoint detector and descriptor

KW - keypoint matching

KW - ORB

KW - relative radiometric normalization (RRN)

KW - SIFT

KW - SURF

UR - http://www.scopus.com/inward/record.url?scp=85103765822&partnerID=8YFLogxK

U2 - 10.1109/JSTARS.2021.3069919

DO - 10.1109/JSTARS.2021.3069919

M3 - Article

AN - SCOPUS:85103765822

VL - 14

SP - 4063

EP - 4073

JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

SN - 1939-1404

M1 - 9392236

ER -