Abstract
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.
Highlights
There is an increasing demand for image sequence analysis, because of the growing use of multi-sensor and multi-temporal remote sensing data to monitor the land-use and land-cover change (LUCC) and climate change, as well as to analyze the global resource environment [1,2,3,4]
The paper focused on the radiometric normalization of satellite image sequence analysis, especially, the elimination of the spectral difference among the images caused by different image acquisition conditions and the effects of the regularly nonlinear spectral differences caused by seasonal variation of natural objects
The method used the kernel canonical correlation analysis transformation to extract NIFs, a set of samples belonging to unchanged area in the kernel space, and to align the relative spectrum of two images using the NIFs-fitted nonlinear relationship
Summary
There is an increasing demand for image sequence analysis, because of the growing use of multi-sensor and multi-temporal remote sensing data to monitor the land-use and land-cover change (LUCC) and climate change, as well as to analyze the global resource environment [1,2,3,4]. Radiometric normalization is a fundamental method used in the pre-processing of satellite image sequence analysis, especially in the pre-processing of change detection [5]. Radiometric normalization can directly make use of the pixel values of an image to establish the corresponding transformation equation for each multi-spectral band in multi-temporal remote sensing data, without the request of any other parameters such as the atmospheric conditions when the remote sensing data obtained [6]. In such a context, radiometric normalization is called spectral alignment [7]. Radiometric normalization builds the common radiometric scale/reference and the radiometric consistency among an image sequence
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