HYBRID CANONICAL CORRELATION ANALYSIS and REGRESSION for RADIOMETRIC NORMALIZATION of CROSS-SENSOR SATELLITE IMAGES

L. G. Denaro, C. H. Lin

Research output: Contribution to journalConference articlepeer-review

Abstract

Relative radiometric normalization (RRN) minimizes radiometric differences among images caused by inconsistencies of acquisition condition. In this study, a cross-sensor RRN method is proposed for optical satellite images from Landsat 8 OLI (L8) and Landsat 7 ETM+ (L7) sensors. The data from these two sensors have different pixel depths. Therefore, a rescaling on the radiometry resolution is performed in the preprocessing. Then, multivariate alteration detection (MAD) based on kernel canonical correlation analysis (KCCA) is adopted, which is called KCCA-based MAD, to select pseudo-invariant features (PIFs). The process of RRN is performed by using polynomial regression with Gaussian weighted regression. In experiments, qualitative and quantitative analyses on images from different sensors are conducted. The experimental result demonstrates the superiority of the proposed nonlinear transformation, in terms of regression quality and radiometric consistency, compared with RRN using linear regression.

Original languageEnglish
Pages (from-to)181-183
Number of pages3
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Volume42
Issue number4/W19
DOIs
Publication statusPublished - 2019 Dec 23
Event2019 Geomatics and Data Science: Towards Adaptive Management in a Changing World, PhilGEOS x GeoAdvances 2019 - Manila, Philippines
Duration: 2019 Nov 142019 Nov 15

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Geography, Planning and Development

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