A Cloud Removal Algorithm to Generate Cloud and Cloud Shadow Free Images Using Information Cloning

One of the main problems of optical remote sensing is clouds and cloud shadows caused by specific atmospheric conditions during data acquisition. These features limit the usage of acquired images and increase the difficulty in data analysis, such as normalized difference vegetation index values, misclassification, and atmospheric correction. Accurate detection and reliable cloning of cloud and cloud shadow features in satellite images are very useful processes for optical remote sensing applications. In this study, an automated cloud removal algorithm to generate cloud and cloud shadow free images from multitemporal Landsat-8 images is introduced. Cloud and cloud shadow areas are classified by using process-based rule set developed by using spectral and spatial features after applying simple linear iterative clustering superpixel segmentation algorithm to the image to find cloud pixel groups easily and correctly. Segmentation-based cloud detection method gives better results than pixel-based for detection of cloud and cloud shadow patches. After detection of clouds and cloud shadows, cloud-free images are created by cloning cloudless regions from multitemporal dataset. Spectral and structural consistency are preserved by considering spectral features and seasonal effects while cloning process. Statistical similarity tests are applied to find best cloud-free image to use for cloning process. Cloning results are tested with the structural similarity index metric to evaluate the performance of cloning algorithm

Exploring the Linkage of Spatial Indicators from Remote Sensing Data with Survey Data—the Case of the Socio-Economic Panel (SOEP) and 3D City Models

This paper demonstrates the spatial evaluation of survey data from the German Socio-Economic Panel (SOEP) study using geo-coordinates and spatially relevant indicators from remote sensing data. By geocoding the addresses of survey households with block-level geographic precision (while preventing their identification by name and guaranteeing their complete anonymity), data on SOEP respondents can now be analyzed in a specific spatial context. In the past, regional analyses of SOEP based on official regional indicators (e.g., the unemployment rate) always had only very imprecise spatial information to work with. This limitation has now been overcome with the geocoded respondents’ information. Within a protected unit of the fieldwork organization responsible for SOEP (TNS Infratest, Munich), the addresses of survey households can now be used to generate a variable describing the location of the household with block-level precision. At DIW Berlin, this additional variable is fed into a special computer infrastructure with multiple security layers that makes the socio-economic analysis possible. This paper demonstrates the use of this geographical location and remote sensing data to check respondents’ subjective assessments of the location of their residence, and discusses the analytical potential of linking remote sensing data and survey data