Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection

Abstract

The radiometric normalization of multitemporal satellite optical images of the same terrain is often necessary for land cover change detection, e.g., relative differences. In previous studies, ground reference data or pseudo-invariant features (PIFs) were used in the radiometric rectification of multitemporal images. Ground reference data are costly and difficult to acquire for most satellite remotely sensed images and the selection of PIFs is generally subjective. In addition, previous research has been focused on radiometric normalization of two images acquired on different dates. The problem of conservation of radiometric resolution in the case of radiometric normalization between more than two images has not been addressed. This article reports on a new procedure for radiometric normalization between multitemporal images of the same area. The selection of PIFs is done statistically. With quality control, principal component analysis (PCA) is used to find linear relationships between multitemporal images of the same area. The satellite images are normalized radiometrically to a common scale tied to the reference radiometric levels. The procedure ensures the conservation of radiometric resolution for the multitemporal images involved. The new procedure is applied to three Landsat-5 Thematic Mapper (TM) images from three different years (August 1986, 1987, and 1991) and of the same area. Quality control measures show that the error in radiometric consistency between the multitemporal images is reduced effectively. The Normalized Difference Vegetation Index (NDVI) is calculated using the radiometrically normalized multitemporal imagery and assessed in the context of land cover change analysis.