Pseudo Invariant Features Selection for Optical Satellite Images Using Multitemporal and Multivariate Alteration Detection

  • 王 志嘉

Student thesis: Master's Thesis

Abstract

Radiometric normalization is a fundamental preprocessing for multitemporal optical satellite images The methods of radiometric normalization can be classified into absolute and relative normalization based on the data required in the algorithm Absolute normalization converts image digital numbers to Earth surface reflectance with the aids of sensor calibration data atmospheric correction model and sun angle which are not always available In contrast relative normalization converts digital numbers of subject images to that of a selected reference image or to a common reference domain without the requirement of additional data However 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 a method called Multivariate Alteration Detection (MAD) was applied to statistically select no-changed pixels in bi-temporal satellite images However MAD is sensitive to significant land-cover changes such as cloud covers Several clouds may be misclassified as PIFs in this method For this reason Iteratively Reweighted MAD (IR-MAD) was introduced to establish a better no-changed background using iterative scheme Nonetheless both MAD and IR-MAD compute linear combinations which are suitable for bi-temporal images only and are 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 for multitemporal and multispectral images The proposed method computes correlation coefficients for not only multivariable data but also multitemporal data Specifically the method integrates the strengths of Generalized Canonical Correlation Analysis (GCCA) and IR-MAD and PIFs are extracted simultaneously from a sequence of satellite images which leads to a consistent PIFs 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 In experiments SPOT-5 imagery was tested Compared with Canonical Correlation Analysis (CCA) which is used in MAD and IR-MAD the proposed method can discriminate no-changed pixels from changed more accurately
Date of Award2017 Aug 31
Original languageEnglish
SupervisorChao-Hung Lin (Supervisor)

Cite this

Pseudo Invariant Features Selection for Optical Satellite Images Using Multitemporal and Multivariate Alteration Detection
志嘉, 王. (Author). 2017 Aug 31

Student thesis: Master's Thesis