Normalization of Multi-Temporal Images Using a New Change Detection Method Based on the Spectral Classifier

Abstract

Orbital images are difficult to maintain a radiometric precision due to the sensor oscillation, atmosphere interferences, season variation of the solar illumination angle, among others. Thus, many radiometric correction techniques have been developed for time series considering mainly: (a) landscape elements whose reflectances are nearly constant over time called of invariants features and (b) linear regression over invariants features assumes that the pixels sampled in the same places at different times are linearly correlated. Therefore, the key problem to the image regression method is an accurate selection of invariant features. In this paper is proposed new radiometric normalization software developed in Turbo C language that searches the highest quality of the invariant features. The algorithm comprises the following steps: (a) identification of the invariant points using a new change detection method based on the spectral classifier algorithms and (b) regression linear between temporal band pairs eliminating the outliers. Initially the algorithm identifies invariants points using a new change detection method based on the spectral classifier algorithms: Spectral Angle Mapper (SAM) and Spectral Correlation Mapper (SCM). In particular, this method approach allows the automatic identification of the invariants points to calibrate remote-sensing images, without visual interpretation data. Program's users establish the spectral change detection method (SAM or SCM) and the threshold value. The second step is to apply two successive linear regressions. First linear regression searches the outlier points using only the pixels with more value than threshold. The outlier points identification use root means square (RMS) and these not include in the second linear regression. Thus, this is last line regression considers only the best spectral for radiometric adjustment. Finally, the gain and offset values are determined and applied for each band in t2 image. In the case of a mistaken selection of points, the program enables to identify a new threshold in both described stages (spectral change detection method and first linear regression).