Radiometric Correction of Multitemporal Satellite Imagery

Abstract

Problem statement: Repeated observation of a given area over time yields potential for many forms of change detection analysis. These repeated observations are confounded in terms of radiometric consistency due to changes in sensor calibration over time, differences in illumination, observation angles and variation in atmospheric effects. Also major problem with satellite images is that regions below clouds are not covered by sensor. Cloud detection, removal and data prediction in cloudy region is essential for image interpretation. Approach: This study demonstrated applicability of empirical relative radiometric normalization methods to a set of multitemporal cloudy images acquired by Resourcesat-1 LISS III sensor. Objective of this study was to detect and remove cloud cover and normalize an image radiometrically. Cloud detection was achieved by using Average Brightness Threshold (ABT) algorithm. The detected cloud removed and replaced with data from another images of the same area. We proposed a new method in which cloudy pixels are replaced with predicted pixel values obtained by regression. After cloud removal, the proposed normalization method was applied to reduce the radiometric influence caused by non surface factors. This process identified landscape elements whose reflectance values are nearly constant over time, i.e., the subset of non-changing pixels are identified using frequency based correlation technique. Further, we proposed another method of radiometric correction in frequency domain, Pseudo-Invariant Feature regression and this process removed landscape elements such as vegetation whose reflectance values are not constant over time. It takes advantage of vegetation being typically high frequency area, can be removed by low pass filter. Results: The quality of radiometric normalization is statistically assessed by R² value and Root Mean Square Error (RMSE) between each pair of analogous band. Further we verified that difference in mean and standard deviation is reduced after normalization of subject image with respect to reference image. Results are compared with commonly used No Change regression method in spatial domain. Conclusion: Cloud removal depends on spatial registration between the two images (reference image and cloudy subject image). Visual inspection shows proposed cloudy pixel prediction method performs better than replacing cloudy pixels with another image of the same area. Statistical analysis also shows that average RMSE of all bands is more in No Change method. Correlation in Fourier domain does not require water body in the scene, while No Change method does require.