In this paper we propose a method to perform automated radiometric correction of remotely sensed multispectral hyperspectral images. The effects of atmosphere, as well as the calibration errors which the satellite sensors may present, may be compensated by performing the radiometric correction operation in order to achieve good performances in different applications, such as classification and change detection. As far as the change detection is concerned, relative radiometric correction is particularly interesting since it deals with images which have to be compared and since in this context an absolute correction may be characterized by a high complexity. One method for performing radiometric correction of multispectral images can be based on a least-square approach: considering one image as the reference one and the other as a linearly scaled version of the reference one, the linear coefficients can be calculated by using a set of control points conveniently chosen. Unfortunately, the choice of control points is a tricky operation, strictly connected to the specific application. In this paper we propose an automated method for performing relative radiometric correction of multispectral remotely sensed images, in which the choice of the control points is based on a comparison of the spectral content of those images to the spectral response of known materials. Specifically, we perform a vector quantization of the images separately, considering N quantization levels represented by N known materials’ signatures properly selected. Then the quantized images are compared in order to identify the areas classified as belonging to the same class, so identified by the same quantization index which will make the subset of control points that should be used for performing relative radiometric correction. Experimental results showed that choosing points characterized by an homogeneous spectral content for radiometric correction improves the performances of specific image processing algorithms, such as change detection and classification algorithms.

Date Published: 8 November 2012
PDF: 9 pages
Proc. SPIE 8537, Image and Signal Processing for Remote Sensing XVIII, 853707 (8 November 2012); doi: 10.1117/12.974680

Published in SPIE Proceedings Vol. 8537:
Image and Signal Processing for Remote Sensing XVIII
Lorenzo Bruzzone, Editor(s)