Details
| Original language | English |
|---|---|
| Article number | 9392236 |
| Pages (from-to) | 4063-4073 |
| Number of pages | 11 |
| Journal | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Volume | 14 |
| Publication status | Published - 31 Mar 2021 |
| Externally published | Yes |
Abstract
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.
Keywords
- AKAZE, BRISK, change detection, KAZE, keypoint detector and descriptor, keypoint matching, ORB, relative radiometric normalization (RRN), SIFT, SURF
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
- Earth and Planetary Sciences(all)
- Atmospheric Science
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In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 14, 9392236, 31.03.2021, p. 4063-4073.
Research output: Contribution to journal › Article › Research › peer review
}
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 -