Multi-temporal Cloud Removing Technique via Adaptive Kalman Filter
2. CLOUD REMOVING SCHEME
Firstly, the two images, the cloud-covered and reference image, are registered and
cropped precisely to bound the corresponding area. Due to the 1-D adaptive Kalman filter (order
32) is used in this paper. Therefore, three fashions of zigzag raster scans have to perform before
applying to the filter as shown in fig.1. Let the scanned clouded image to be a corrupted
measurement input signal and setting the scanned reference image to be a predicted state, the
reconstructed image can be got by the estimated output of adaptive Kalman filter. Finally, three
output filters are averaged to reduce the uncertainty in each raster scans.
Fig.1 The proposed scheme
