LIRRN

Location-Independent Relative Radiometric Normalization of Bitemporal Remote-Sensing Images

verfasst von

Armin Moghimi, Vahid Sadeghi, Amin Mohsenifar, Turgay Celik, Ali Mohammadzadeh

Abstract

Relative radiometric normalization (RRN) is a critical pre-processing step that enables accurate comparisons of multitemporal remote-sensing (RS) images through unsupervised change detection. Although existing RRN methods generally have promising results in most cases, their effectiveness depends on specific conditions, especially in scenarios with land cover/land use (LULC) in image pairs in different locations. These methods often overlook these complexities, potentially introducing biases to RRN results, mainly because of the use of spatially aligned pseudo-invariant features (PIFs) for modeling. To address this, we introduce a location-independent RRN (LIRRN) method in this study that can automatically identify non-spatially matched PIFs based on brightness characteristics. Additionally, as a fast and coregistration-free model, LIRRN complements keypoint-based RRN for more accurate results in applications where coregistration is crucial. The LIRRN process starts with segmenting reference and subject images into dark, gray, and bright zones using the multi-Otsu threshold technique. PIFs are then efficiently extracted from each zone using nearest-distance-based image content matching without any spatial constraints. These PIFs construct a linear model during subject–image calibration on a band-by-band basis. The performance evaluation involved tests on five registered/unregistered bitemporal satellite images, comparing results from three conventional methods: histogram matching (HM), blockwise KAZE, and keypoint-based RRN algorithms. Experimental results consistently demonstrated LIRRN’s superior performance, particularly in handling unregistered datasets. LIRRN also exhibited faster execution times than blockwise KAZE and keypoint-based approaches while yielding results comparable to those of HM in estimating normalization coefficients. Combining LIRRN and keypoint-based RRN models resulted in even more accurate and reliable results, albeit with a slight lengthening of the computational time. To investigate and further develop LIRRN, its code, and some sample datasets are available at link in Data Availability Statement.

Organisationseinheit(en)

Ludwig-Franzius-Institut für Wasserbau, Ästuar- und Küsteningenieurwesen

Externe Organisation(en)

K.N. Toosi University of Technology
University of Tabriz
University of the Witwatersrand
University of Agder

Typ

Artikel

Journal

Sensors

Band

24

Anzahl der Seiten

18

ISSN

1424-3210

Publikationsdatum

02.04.2024

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Analytische Chemie, Information systems, Atom- und Molekularphysik sowie Optik, Biochemie, Instrumentierung, Elektrotechnik und Elektronik

Ziele für nachhaltige Entwicklung

SDG 15 – Lebensraum Land

Elektronische Version(en)

https://doi.org/10.3390/s24072272 (Zugang: Offen)