Abstract
Characteristics of impulse bursts in satellite images are analyzed and methods for burst removal are consid- ered. Artificial compact burst model is proposed and test images are created. An advanced multipass algorithm for the detection and removal of compact bursts in the presence of both additive and multiplicative noise is proposed. The efficiency of the algorithm is evaluated quantitatively using the artificial test images and visually using the artificial test images and real radar and optical satellite images. It is shown through experiments that the proposed method removes impulse bursts efficiently while preserving information.
Highlights
Impulsive noise is one of the factors that degrade the quality and interpretation accuracy of remote sensing (RS) images
If the communication line is poor, impulse bursts can be observed on the transmitted images even through a digital line that uses noise-stable coding
By estimating the statistical characteristics of the bursts, we found that the mean of the burst fragments varied from 160 to 190 with cut-off effects observed for bright burst pixels
Summary
Impulsive noise is one of the factors that degrade the quality and interpretation accuracy (especially automatic interpretation) of remote sensing (RS) images. The probability of the occurrence of a burst is much higher if an analog transmission line is used because such lines usually do not have any special means for the protection against the interference of the parasite signal An example of such situation is the old satellite image transmission standard, the so-called “automatic picture transmission” (APT) mode. Applying the method [6] for the CIB removal results in a considerable distortion for a large number of pixels in satellite image rows that are not corrupted by bursts. A multistage method for the CIB detection and removal in the presence of both additive and multiplicative noise was proposed in our paper [7] This approach eliminates CIBs rather reliably and at the same time appropriately preserves information contained in the images. It consists of several stages and uses spatial features permitting to distinguish CIBs and horizontally oriented small size objects
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