Abstract
This paper presents a processing chain for handling big volume of remotely sensing data for generating wide extent mosaics. More specifically, the data under consideration are level-1 ground range detected Sentinel-1 products with dual polarisation (VV+VH or HH+HV). Two approaches for a) distribution discretization accompanied by false color composition and b) image rendering and mosaicking are proposed. While these two components are necessary constituents of the presented mosaicking workflow, they can operate independently of each other. The design of the processing chain satisfies three objectives: i) contrasting derivative products of the input Sentinel-1 imagery such as the Global Human Settlement Layer, ii) adapting on a high-throughput computing system for fast execution, and iii) allowing potential extensions to more complex applications such as the image classification. Fast processing, process automation, incremental adjustment and information distinction are the main advantages of the proposed method. Elaboration and focus on these features are carried out during the presentation of the results.
Highlights
LARGE scale mosaics of satellite data are crucial for several applications that involve geo-analysis with remote sensing data such as mapping large natural hazard areas [1], landcover classification [2] and for guiding field investigations [3]
This mosaic has been produced with the objective to facilitate visual assessment of built-up areas automatically extracted from Sentinel-1 data in the framework of the Global Human Settlement Layer (GHSL)
Apart from the main objective of the produced Sentinel-1 mosaic to play the role of a base layer suitable for visual inspection and validation of other derived layers, we investigate the possibility whether such a layer might aid to a rapid image classification
Summary
LARGE scale mosaics of satellite data are crucial for several applications that involve geo-analysis with remote sensing data such as mapping large natural hazard areas [1], landcover classification [2] and for guiding field investigations [3]. Besides being useful for the automatic extraction of information on built-up areas, the Sentinel-1A collection could be used for generating an RGB false-colour mosaic in support to visual assessment of the results of the built-up detection The provision of such a base map offering a userfriendly representation of the physical information is a necessary condition for the full exploitation of the Sentinel-1 sensor. None of the recently produced national and regional mosaics of Sentinel-1 data (Romania, Germany, Europe [12], etc.), covers the needs for: i) enhanced interpretability of built-up areas, ii) fully automated processing chain suitable for being executed in high-throughput computing facility, and the most important iii) global coverage To answer those needs, an algorithmic workflow is proposed for building and mosaicking false colour composites based on a global coverage of Sentinel-1 data. Preliminary results were presented at the 2017 Big Data from Space conference [13]
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