Removal of Optically Thick Clouds from Multi-Spectral Satellite Images Using Multi-Frequency SAR Data
AbstractThis study presents a method for the reconstruction of pixels contaminated by optical thick clouds in multi-spectral Landsat images using multi-frequency SAR data. A number of reconstruction techniques have already been proposed in the scientific literature. However, all of the existing techniques have certain limitations. In order to overcome these limitations, we expose the Closest Spectral Fit (CSF) method proposed by Meng et al. to a new, synergistic approach using optical and SAR data. Therefore, the term Closest Feature Vector (CFV) is introduced. The technique facilitates an elegant way to avoid radiometric distortions in the course of image reconstruction. Furthermore the cloud cover removal is independent from underlying land cover types and assumptions on seasonality, etc. The methodology is applied to mono-temporal, multi-frequency SAR data from TerraSAR-X (X-Band), ERS (C-Band) and ALOS Palsar (L-Band). This represents a way of thinking about Radar data not as foreign, but as additional data source in multi-spectral remote sensing. For the assessment of the image restoration performance, an experimental framework is established and a statistical evaluation protocol is designed. The results show the potential of a synergistic usage of multi-spectral and SAR data to overcome the loss of data due to cloud cover. View Full-Text
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Eckardt, R.; Berger, C.; Thiel, C.; Schmullius, C. Removal of Optically Thick Clouds from Multi-Spectral Satellite Images Using Multi-Frequency SAR Data. Remote Sens. 2013, 5, 2973-3006.
Eckardt R, Berger C, Thiel C, Schmullius C. Removal of Optically Thick Clouds from Multi-Spectral Satellite Images Using Multi-Frequency SAR Data. Remote Sensing. 2013; 5(6):2973-3006.Chicago/Turabian Style
Eckardt, Robert; Berger, Christian; Thiel, Christian; Schmullius, Christiane. 2013. "Removal of Optically Thick Clouds from Multi-Spectral Satellite Images Using Multi-Frequency SAR Data." Remote Sens. 5, no. 6: 2973-3006.