RELATIVE RADIOMETRIC NORMALIZATION OF MULTITEMPORAL IMAGES
This design is based on analyzes results of the application of an automatic method of radiometric normalization between two multi-temporal images of the same zone. This radiometric adjustment is part of the pre-processing of image changes detection. Any surface in two images recorded with the same sensor should ideally appear with similar values in their digital levels, but in real practice, it doesn’t happen due to several reasons, among them different atmospheric conditions, and different lighting from different recorded dates. That is the reason why pixels from the same terrain can show different radiance values, and, therefore, different values in their digital levels. In satellite images, radiometric normalization must determine ground absolute reflectivity through correction algorithms as well as atmospheric properties related to the moment of the acquisition of the image. For aerial images (in which atmospheric effects are not as prominent as in satellite images), and for many applications of change detection lineal radiometric normalization of multi-temporal is enough. To this end, one of the images is taken as reference and the necessary radiometric correction is applied to the other in order to make the tone of its pixels with those of the reference image. The MAD (Multivariate Alteration Detection) transformation applied to both images from different times is invariant to arbitrary linear transformations of the intensities of the pixels involved in the transformation. That is the reason why in the implementation of the change detection method (MAD) pre-processing with radiometric normalization is superfluous. This design based on the combined use of MAD transformation applied to not-normalized multi-temporal images to select NOT-changed pixels and then their utilization for a relative radiometric normalization.
Reference Paper: Relative Radiometric Normalization of Multitemporal images
Author’s Name: Carlos Javier Broncano Mateos,Carlos Pinilla Ruiz,Rubén González Crespo,and Andres Castillo Sanz
Source: IJIMAI
Year:2010
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