Radiometric Normalization of Ikonos Image Using Quickbird Image for Urban Area Change Detection

Remotely sensed multitemporal, multisensor data are often required for change detection applications. A common problem associated with the use of these data is the grey value difference caused by non-surface factors such as different illumination, atmospheric, or sensor conditions. Such a difference makes it difficult to accurately detect changes using automatic methods. Effective image normalization is, therefore, important to reduce the radiometric influence caused by the non-surface factors and to ensure that the grey value difference between two temporal images reflects actual changes on the surface of the Earth. A variety of image normalization methods, which include pseudo-invariant features (PIF), dark and bright set (DB), simple regression (SR), no-change set determined from scattergrams (NC), and histogram matching (HM), have been published in scientific journals. These methods have been tested with either Landsat TM data, MSS data or both, and show different results varying from authors to authors. However, whether the existing methods would be adopted for normalizing currently available high resolution multispectral satellite images, such as IKONOS and QuickBird, the question is still open because of the drastic change in spatial resolution and difference of available multispectral bands. In this research, the existing methods are introduced and employed to normalize the radiometric difference between IKONOS and QuickBird multispectral images taken in different years. Some improvements are introduced to overcome problems caused by band difference and to achieve more stable and better results. The normalized results are compared in terms of visual inspection and statistical analysis.