Radiometric Correction of Multitemporal Satellite Imagery

Abstract

<b>Problem statement:</b> 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. <b>Approach:</b> 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. <b>Results:</b> 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. <b>Conclusion:</b> 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.</font

A new automatic regression-based approach for relative radiometric normalization of multitemporal satellite imagery

“…A variety of RRN methods has been provided for the relative radiometric normalization of multispectral images taken under divergent conditions at different times (Yang and Lo 2000;Hall et al 1991;Ya'allah and Saradjian 2005;Jensen and Cowen 1999;Chavez 1988;Biday and Bhosle 2010). Histogram matching (HM) method has been widely used in relative radiometric normalization of satellite imagery.…”

Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection

“…In addition, the obtained no-change set should have a correlation coefficient greater than 0.9 [12,13]. In this study, the center coordinate of the water cluster in the scattergram was (5,5), and the center coordinate of the land surface in the scattergram was (71, 88).…”

“…Simple regression (SR) normalization uses least-squares to derive the normalization coefficients. The SR normalization coefficients are solved from Equation (3) [12,25]. Thus,…”

“…The absolute method aims to convert the digital numbers recorded by satellite sensors to the true surface reflectance and correct for factors such as changes in the satellite sensor, solar angle, and atmospheric influence [9][10][11]. However, absolute radiometric normalization often presents difficulties with atmospheric data collection in terms of cost and accessibility, and cannot be implemented when there are no ground-measured values to obtain the detection data; therefore, in most cases, relative radiometric normalization is utilized [10,12].…”

“…Relative radiometric normalization does not require complex atmospheric transfer parameterization processes and can yield information on the relative changes contained in multi-temporal image data that are tracked and corrected so that areas with changes can be identified easily and quickly [12]. One commonly used method is a mathematical model wherein data are corrected through regression equations, assuming a linear relationship between bands in multi-temporal image data, provided that the area is the same [13,14].…”