Next Article in Journal
A Topography-Informed Morphology Approach for Automatic Identification of Forest Gaps Critical to the Release of Avalanches
Previous Article in Journal
An Improved Single-Channel Method to Retrieve Land Surface Temperature from the Landsat-8 Thermal Band
Open AccessArticle

kCCA Transformation-Based Radiometric Normalization of Multi-Temporal Satellite Images

by Yang Bai 1,2, Ping Tang 2 and Changmiao Hu 2,*
1
University of the Chinese Academy of Sciences, Beijing 100049, China
2
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(3), 432; https://doi.org/10.3390/rs10030432
Received: 14 February 2018 / Revised: 5 March 2018 / Accepted: 6 March 2018 / Published: 10 March 2018
Radiation normalization is an essential pre-processing step for generating high-quality satellite sequence images. However, most radiometric normalization methods are linear, and they cannot eliminate the regular nonlinear spectral differences. Here we introduce the well-established kernel canonical correlation analysis (kCCA) into radiometric normalization for the first time to overcome this problem, which leads to a new kernel method. It can maximally reduce the image differences among multi-temporal images regardless of the imaging conditions and the reflectivity difference. It also perfectly eliminates the impact of nonlinear changes caused by seasonal variation of natural objects. Comparisons with the multivariate alteration detection (CCA-based) normalization and the histogram matching, on Gaofen-1 (GF-1) data, indicate that the kCCA-based normalization can preserve more similarity and better correlation between an image-pair and effectively avoid the color error propagation. The proposed method not only builds the common scale or reference to make the radiometric consistency among GF-1 image sequences, but also highlights the interesting spectral changes while eliminates less interesting spectral changes. Our method enables the application of GF-1 data for change detection, land-use, land-cover change detection etc. View Full-Text
Keywords: kernel version of canonical correlation analysis; radiometric normalization; Gaofen-1 satellite; nonlinear invariant features kernel version of canonical correlation analysis; radiometric normalization; Gaofen-1 satellite; nonlinear invariant features
Show Figures

Graphical abstract

MDPI and ACS Style

Bai, Y.; Tang, P.; Hu, C. kCCA Transformation-Based Radiometric Normalization of Multi-Temporal Satellite Images. Remote Sens. 2018, 10, 432.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop