Radiometric normalization of multitemporal satellite images using weighted principal component analysis

Bo Yi Lin, Kuan Yi Lee, Chao Hung Lin

Research output: Contribution to conferencePaperpeer-review

Abstract

The radiometric normalization of multitemporal satellite is a fundamental and important process for land cover change detection. In previous studies, ground reference data or pseudo-invariant features (PIFs) were used in the radiometric normalization. However, ground reference data are difficult to acquire and the selection of PIFs is generally sensitive to cloud covers. In this paper, an approach based on weighted principal component analysis is proposed for PIFs selection, which can withstand the disturbance of cloud covers. In the experiments, qualitative analyses of image sequences acquired by Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor and quantitative analyses of image sequences with various cloud contamination conditions and landscapes are conducted to evaluate the proposed method. The experimental results show that the proposed radiometric normalization has the ability to deal with images that contain various clouds.

Original languageEnglish
Publication statusPublished - 2015 Jan 1
Event36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, ACRS 2015 - Quezon City, Metro Manila, Philippines
Duration: 2015 Oct 242015 Oct 28

Other

Other36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, ACRS 2015
CountryPhilippines
CityQuezon City, Metro Manila
Period15-10-2415-10-28

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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