Removal of Optically Thick Clouds from Multi-Spectral Satellite Images Using Multi-Frequency SAR Data

Abstract

This 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.

Application of Landsat-8 and ALOS-2 data for structural and landslide hazard mapping in Kelantan, Malaysia

“…Isolated noise pixels can, therefore, be removed with a median filter (Schowengerdt, 2007). The median filter is especially useful for removing shot noise (pixel values with no relation to the image scene) and has certain advantages, such not shifting boundaries and minimal degradation to edges (Eliason and McEwen, 1990;Russ, 2002;Jensen, 2005). In this study, a 3 × 3 neighborhood convolution mask (kernel) was applied to the PALSAR images.…”

“…Mapping landslides in tropical environments is difficult because landscapes are generally covered by thick tropical rainforest (Plank et al, 2016). In addition, the cloudy weather and rain affected the quality of the optical satellite remote sensing data (Melgani, 2006;Ju and Roy 2008;Ramli et al, 2009;Razak et al, 2013;Eckardt et al, 2013;Shahabi and Hashim, 2015). The main target of this investigation is to identify high potential risk and susceptible zones in the Kelantan River basin for mapping landslide occurrence zone using satellite remote sensing technology and geographic information system (GIS) techniques.…”

Radar Time Series for Land Cover and Forest Mapping

“…With ripening and yellowing, attenuation is minimized and backscatter increases again. This process is especially observable at C-band because of the correspondence in size between the wavelength and geometry of the scattering objects (Eckardt et al 2013). The radar scenes are being used as an additional source of spectral information to overcome the problem of missing data due to cloud cover wall products from radar backscatter retrievals using "hyper-temporal" statistics, a term introduced for radar applications by Schmullius and Santoro in 2007. One of the first hyper-temporal retrievals for land cover applications had been performed with a non-imaging system, the wind scatterometer on-board ERS-1 for northern Eurasia (Schmullius 1997).…”

“…Eckardt et al (2013) reconstructed cloud-contaminated pixels in Landsat images using the Synthetic Aperture Radar (SAR) data. Cloud contamination was simulated with masks of varying size to systematically investigate the developed technique.…”

Optical Cloud Pixel Recovery via Machine Learning

“…This is especially significant in change detection analyses at climatic time scales. Errors due to the presence of clouds introduce uncertainties in satellite images during information retrieval, signal processing, data compression and distribution procedures causing anomalous results that are often difficult to correct [9,10].…”