Katrin Molch

Identifying damage caused by the 2008 Wenchuan earthquake from VHR remote sensing data

Abstract The paper discusses the potential of very high resolution (VHR) satellite imagery for post-earthquake damage assessment in comparison with the role of aerial photographs. Post-disaster optical and radar satellite data are assessed for their ability to resolve collapsed buildings, destroyed transportation infrastructure, and specific land cover changes. Optical VHR imagery has shown to be effective in quantifying building stock and for assessing damage at the building level. High-resolution synthetic aperture radar (SAR) imagery requires further research to identify optimum information extraction procedures for rapid assessment of affected buildings. Based on current technical and operational capabilities increasing efforts should be devoted to the generation of spatial datasets for disaster preparedness.

Anisotropic Rotation Invariant Built-Up Presence Index: Applications to SAR Data

The results shown in this paper highlights the usefulness of a recently proposed index to extract hints of built-up areas in remotely sensed images. The novelty of this work is in the application of the approach to a very different data set than the one for which the index was originally developed, i.e. SAR instead of optical data. Due to the different approaches (active vs. passive sensors), wavelengths (optical vs. microwave) and distortion/noise effects (additive vs. speckle noise), it is valuable to find out the advantages and limits of the index results on these new data sets. Moreover, due to the different geometry of acquisition for radar sensors, two different implementations of the same index are considered and compared, adding insights on the suitability of slant-range vs. ground-range analysis of SAR data for built-up area recognition.

Geological case studies related to RADARSAT-2

RADARSAT-2 has several capabilities that will be useful to geologists. Some of these capabilities include the availability of high-resolution 3 m synthetic aperture radar (SAR) images, multipolarization and fully polarimetric image modes, and left- and right-looking images. These capabilities are currently being evaluated. Our early results from several ongoing case studies have shown that RADARSAT-2 images will improve current structural and terrain image mapping techniques; provide high-resolution mineral exploration property maps from image fusion techniques; improve interferometric SAR (InSAR) deformation monitoring, particularly small active landslides; and possibly improve the classification of surficial sediments.

Building Virtual Earth Observatories Using Ontologies, Linked Geospatial Data and Knowledge Discovery Algorithms

Advances in remote sensing technologies have allowed us to send an ever-increasing number of satellites in orbit around Earth. As a result, satellite image archives have been constantly increasing in size in the last few years (now reaching petabyte sizes), and have become a valuable source of information for many science and application domains (environment, oceanography, geology, archaeology, security, etc.). TELEIOS is a recent European project that addresses the need for scalable access to petabytes of Earth Observation data and the discovery of knowledge that can be used in applications. To achieve this, TELEIOS builds on scientific databases, linked geospatial data, ontologies and techniques for discovering knowledge from satellite images and auxiliary data sets. In this paper we outline the vision of TELEIOS (now in its second year), and give details of its original contributions on knowledge discovery from satellite images and auxiliary datasets, ontologies, and linked geospatial data.

Building virtual earth observatories using ontologies and linked geospatial data

Advances in remote sensing technologies have enabled public and commercial organizations to send an ever-increasing number of satellites in orbit around Earth. As a result, Earth Observation (EO) data has been constantly increasing in volume in the last few years, and is currently reaching petabytes in many satellite archives. For example, the multi-mission data archive of the TELEIOS partner German Aerospace Center (DLR) is expected to reach 2PB next year, while ESA estimates that it will be archiving 20PB of data before the year 2020. As the volume of data in satellite archives has been increasing, so have the scientific and commercial applications of EO data. Nevertheless, it is estimated that up to 95% of the data present in existing archives has never been accessed, so the potential for increasing exploitation is very big.

InSAR Monitoring of Post-Landslide Activity

In this study we used differential InSAR techniques to monitor current post slide activity at several landslides along transportation and energy corridors. The landslide materials vary from rock debris, glacial till to permafrost alluvium. Our results show that motion is triggered by spring melt and heavy rainfall events. In the northern Mackenzie Valley pipeline corridor seasonal landslide activity is related to permafrost melt during warm summer months.

Performance of Built-up Area Classifications Using High-Resolution SAR Data

Identification of the built-up area from satellite imagery can provide a crucial information layer in disaster mitigation and management and for monitoring urban sprawl, e.g., in developing countries. Spaceborne radar imagery is at an advantage in regions where environmental conditions impede the acquisition of optical image data. Automated exploitation procedures are imperative for efficient, large-area coverage. However, methodologies must be developed or adapted to account for the specific characteristics of synthetic aperture radar (SAR) data. This study evaluates the identification of the built-up area on RADARSAT-1 fine mode and Environmental Satellite (ENVISAT) image mode data using the texture-based, anisotropic, rotation-invariant built-up presence index. Data selection and processing parameters are discussed. User’s accuracies of up to 77.5% and overall accuracies of up to 81.3% were achieved in this comparative study without any postclassification editing.

Big Data Management in Earth Observation: The German satellite data archive at the German Aerospace Center

The German Satellite Data Archive (D-SDA) at the German Aerospace Center (DLR) has been managing largevolume Earth-observation (EO) data in the context of EOmission payload ground segments (PGSs) for more than two decades. Hardware, data management, processing, user access, long-term preservation, and data exploitation expertise are under one roof and interact closely. Upcoming EO-mission PGSs benefit as much from the comprehensive expertise, close interaction, and integrated infrastructure as do in-house scientific application projects requiring access, processing, and archiving of large-volume EO data. Using a number of examples, we will demonstrate how EO data life cycles benefit from the proximity of data management and application scientists and from the extensive operational experience gathered over time.

Big data management in Earth Observation: the German Satellite Data Archive at DLR

This paper presents a Near-Real-Time multi-GPU accelerated solution of the ωk Algorithm for Synthetic Aperture Radar (SAR) data focusing, obtained in Stripmap SAR mode. Starting from an input raw data, the algorithm subdivides it in a grid of a configurable number of bursts along track. A multithreading CPU-side support is made available in order to handle each graphic device in parallel. Then each burst is assigned to a separate GPU and processed including Range Compression, Stolt Mapping via ChirpZ and Azimuth Compression steps. We prove the efficiency of our algorithm by using Sentinel-1 raw data (approx. 3.3 GB) on a commodity graphics card; the single-GPU solution is approximately 4x faster than the industrial multi-core CPU implementation (General ACS SAR Processor, GASP), without significant loss of quality. Using a multi-GPU system, the algorithm is approximately 6x faster with respect to the CPU processor.For decades, field help in case of disasters on the Earth’s surface - like floods, fires or earthquakes - is supported by the analysis of remotely sensed data. In recent years, the monitoring of vehicles, buildings or areas fraught with risk has become another major task for satellite-based crisis intervention. Since these scenarios are unforeseen and time-critical, they require a fast and well coordinated reaction. If useful information is extracted out of image data in realtime directly on board a spacecraft, the timespan between image acquisition and an appropriate reaction can be shortened significantly. Furthermore, on board image analysis allows data of minor interest, e.g. cloud-contaminated scenes, to be discarded and/or treated with lower priority, which leads to an optimized usage of storage and downlink capacity. This paper describes the modular application framework of VIMOS, an on board image processing experiment for remote sensing applications. Special focus will be on resource management, safety and modular commandability.Gaia is an ESA cornerstone mission, which was successfully launched December 2013 and commenced operations in July 2014. Within the Gaia Data Processing and Analysis consortium, Coordination Unit 7 (CU7) is responsible for the variability analysis of over a billion celestial sources and nearly 4 billion associated time series (photometric, spectrophotometric, and spectroscopic), encoding information in over 800 billion observations during the 5 years of the mission, resulting in a petabyte scale analytical problem. In this article, we briefly describe the solutions we developed to address the challenges of time series variability analysis: from the structure for a distributed data-oriented scientific collaboration to architectural choices and specific components used. Our approach is based on Open Source components with a distributed, partitioned database as the core to handle incrementally: ingestion, distributed processing, analysis, results and export in a constrained time window.The seamless mosaicing of massive very high resolution imagery addresses several aspects related to big data from space. Data volume is directly proportional to the size the input data, i.e., order of several TeraPixels for a continent. Data velocity derives from the fact that the input data is delivered over several years to meet maximum cloud contamination constraints with the considered satellites. Data variety results from the need to collect and integrate various ancillary data for cloud detection, land/sea mask delineation, and adaptive colour balancing. This paper details how these 3 aspects of big data are handled and illustrates them for the creation of a seamless pan-European mosaic from 2.5m imagery (Land Monitoring/Urban Atlas Copernicus CORE 03 data set).The current development of satellite imagery means that a great volume of images acquired globally has to be understood in a fast and precise manner. Processing this large quantity of information comes at the cost of finding unsupervised algorithms to fulfill these tasks. Change detection is one of the main issues when talking about the analysis of satellite image time series (SITS). In this paper, we propose a method to analyze changes in SITS based on binary descriptors and on the Hamming distance, regarded as a similarity metric. In order to render an automatic and completely unsupervised technique towards solving this problem, the obtained distances are quantized into change levels using the Lloyd-Max’s algorithm. The experiments are carried on 11 Landsat images at 30 meters spatial resolution, covering an area of approximately 59 × 51 km2 over the surroundings of Bucharest, Romania, and containing information from six subbands of frequency.The Euclid Archive System prototype is a functional information system which is used to address the numerous challenges in the development of fully functional data processing system for Euclid. The prototype must support the highly distributed nature of the Euclid Science Ground System, with Science Data Centres in at least eight countries. There are strict requirements both on data quality control and traceability of the data processing. Data volumes will be greater than 10 Pbyte, with the actual volume being dependent on the amount of reprocessing required.In the space domain, all scientific and technological developments are accompanied by a growth of the number of data sources. More specifically, the world of observation knows this very strong acceleration and the demand for information processing follows the same pace. To meet this demand, the problems associated with non-interoperability of data must be efficiently resolved upstream and without loss of information. We advocate the use of linked data technologies to integrate heterogeneous and schema-less data that we aim to publish in the 5 stars scale in order to foster their re-use. By proposing the 5 stars data model, Tim Berners-Lee drew the perfect roadmap for the production of high quality linked data. In this paper, we present a technological framework that allows to go from raw, scattered and heterogeneous data to structured data with a well-defined and agreed upon semantics, interlinked with other dataset for their common objects.Reference data sets, necessary to the advancement of the field of object recognition by providing a point of comparison for different algorithms, are prevalent in the field of multimedia. Although sharing the same basic object recognition problem, in the field of remote sensing there is a need for specialized reference data sets. This paper would like to open the topic for discussion, by taking a first attempt at creating a reference data set for a satellite image. In doing so, important differences between annotating photographic and satellite images are highlighted, along with their impact on the creation of a reference data set. The results are discussed with a view toward creating a future methodology for the manual annotation of satellite images.The future atmospheric composition Sentinel missions will generate two orders of magnitude more data than the current missions and the operational processing of these big data is a big challenge. The trace gas retrieval from remote sensing data usually requires high-performance radiative transfer model (RTM) simulations and the RTM are usually the bottleneck for the operational processing of the satellite data. To date, multi-core CPUs and also Graphical Processing Units (GPUs) have been used for highly intensive parallel computations. In this paper, we are comparing multi-core and GPU implementations of an RTM based on the discrete ordinate solution method. With GPUs, we have achieved a 20x-40x speed-up for the multi-stream RTM, and 50x speed-up for the two-stream RTM with respect to the original single-threaded CPU codes. Based on these performance tests, an optimal workload distribution scheme between GPU and CPU is proposed. Finally, we discuss the performance obtained with the multi-core-CPU and GPU implementations of the RTM.The effective use of Big Data in current and future scientific missions requires intelligent data handling systems which are able to interface the user to complicated distributed data collections. We review the WISE Concept of Scientific Information Systems and the WISE solutions for the storage and processing as applied to Big Data.Interactive visual data mining, where the user plays a key role in learning process, has gained high attention in data mining and human-machine communication. However, this approach needs Dimensionality Reduction (DR) techniques to visualize image collections. Although the main focus of DR techniques lays on preserving the structure of the data, the occlusion of images and inefficient usage of display space are their two main drawbacks. In this work, we propose to use Non-negative Matrix Factorization (NMF) to reduce the dimensionality of images for immersive visualization. The proposed method aims to preserve the structure of data and at the same time reduce the occlusion between images by defining regularization terms for NMF. Experimental validations performed on two sets of image collections show the efficiency of the proposed method in respect to controlling the trade-off between structure preserving and less occluded visualization.This article provides a short overview about the TanDEM-X mission, its objectives and the payload ground segment (PGS) based on data management, processing systems and long term archive. Due to the large data volume of the acquired and processed products a main challenge in the operation of the PGS is to handle the required data throughput, which is a new dimension for the DLR PGS. To achieve this requirement, several solutions were developed and coordinated. Some of them were more technical nature whereas others optimized the workflows.Clustering of Earth Observation (EO) images has gained a high amount of attention in remote sensing and data mining. Here, each image is represented by a high-dimensional feature vector which could be computed as the results of coding algorithms of extracted local descriptors or raw pixel values. In this work, we propose to learn the features using discriminative Nonnegative Matrix factorization (DNMF) to represent each image. Here, we use the label of some images to produce new representation of images with more discriminative property. To validate our algorithm, we apply the proposed algorithm on a dataset of Synthetic Aperture Radar (SAR) and compare the results with the results of state-of-the-art techniques for image representation. The results confirm the capability of the proposed method in learning discriminative features leading to higher accuracy in clustering.

Identifying SAR permeability zones on groundwater recharge areas

High-resolution multidate SAR spring images with similar incidence angles were used to update permeability maps over recharge areas on glacial aquifers. From a difference image produced from two dates in early spring we interpreted high medium and low permeability zones. These SAR permeability zones are related to the distribution and behavior of soil moisture on different surficial deposits and slopes. Permeability thematic maps will be useful to identify nutrient infiltration patterns and accumulation on farmed recharge areas.