Relative Radiometric Correction of Multitemporal Satellite Imagery Using Fourier and Wavelet Transform

Remotely sensed multitemporal, multisensor data are often required in Earth observation applications. A common problem associated with the use of multisource image data is the grey value differences caused by non-surface factors such as different illumination, atmospheric, or sensor conditions. Image normalization is required to reduce the radiometric influences caused by non-surface factors and to ensure that the grey value differences between temporal images reflect actual changes on the surface of the Earth. This paper proposes new approaches to radiometric correction of multitemporal satellite imagery using wavelet analysis and Fourier transform. The results obtained are compared with commonly used NO-change regression (NC) normalization and Histogram Matching spatial domain methods. Both visual inspection and statistical accuracy assessment show that proposed transform domain approaches are valid.