Radiometric normalization of multitemporal optical satellite images using iteratively-reweighted multivariate alteration detection

Bo Yi Lin, Zhi Jia Wang, Chao Hung Lin

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

Abstract

Radiometric normalization is a fundamental process for multitemporal satellite images. The accuracy of relative normalization depends on the quality of selected Pseudo Invariant Features (PIFs). PIFs represent the ground objects whose reflectance are constant during a period of time. In previous study, an algorithm, called Multivariate Alteration Detection (MAD), was applied to statistically select no-changed pixels within bi-temporal satellite images. However, MAD is sensitive to cloud covers and some clouds may be misclassified as PIFs. For this reason, Iteratively Reweighted MAD (IR-MAD) was introduced to establish an increasingly better no-changed background using iterative scheme. Nonetheless, both MAD and IR-MAD only compute the linear combinations for bi-temporal images, and not applicable for multitemporal images with more than two images. In this study, a novel method called Weighted Generalized Canonical Correlation Analysis (WGCCA) is proposed for the selection of high-quality PIFs in multitemporal and multispectral images, which solves coefficients for the correlations of not only multivariable data but also multitemporal data. Specifically, the proposed method integrates the strengths of Generalized Canonical Correlation Analysis (GCCA) and IR-MAD, and PIFs extraction from a sequence of satellite images is performed at the same time, which leads to a consistent feature extraction. Furthermore, when the high-quality PIFs are determined by the proposed method, the digital numbers of PIFs from multitemporal images are transformed into a predefined radiometric reference level. With this approach, the radiometric resolution of multitemporal images can be preserved, and a better radiometric normalization can be obtained. In experiment, SPOT-5 imagery was tested. Compared with Canonical Correlation Analysis (CCA) which is used in MAD, the proposed method can discriminate no-changed pixels from changed more precisely.

Original languageEnglish
Publication statusPublished - 2017 Jan 1
Event38th Asian Conference on Remote Sensing - Space Applications: Touching Human Lives, ACRS 2017 - New Delhi, India
Duration: 2017 Oct 232017 Oct 27

Other

Other38th Asian Conference on Remote Sensing - Space Applications: Touching Human Lives, ACRS 2017
CountryIndia
CityNew Delhi
Period17-10-2317-10-27

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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