SNR Evaluation of the RapidEye Space-borne Cameras

Summary: After launch and continuous radiation exposure, space-borne cameras are constantly changing. Therefore permanent technical specification and evaluation of the sensor in space plays an important role in the remote sensing community. There are a variety of evaluation criteria, which are all based on the essential camera parameters – the spatial resolution, point spread function (PSF) and noise. Noise estimation is a challenging task for characterization of remote sensing systems in space. The in-flight measurement of noise will often be done with artificial test sites. If these test sites are not suffi-ciently available, homogeneous image regions (desert, snow, water surfaces) are often used. The al-bedo of these objects, however, lies normally outside the specified albedo range of remote sensing systems focused on the Earth's surface. The only possibility to determine the noise after the satellite launch within the normal operational albedo range is to use normal surface objects within the oper-ationally acquired imagery. As these objects have to be homogeneous, one needs methods that can detect the smallest homogeneous areas in the image to evaluate noise. In this paper an approach for determining the signal to noise ratio (SNR) with data from natural tar-gets is presented. In experiments, the results demonstrate that the described method performs well and results are comparable to the standard methods used to determine SNR

Spatial resolution assessment from real image data

The radiometric and spatial characteristics of a remote sensing system specify the image quality. The determination of the image quality is carried out on specific resolution targets. The point spread function (PSF) is an essential parameter for characterizing the spatial imaging properties of the whole system. We present an approach for PSF determination using high-contrast edges found in the typical urban scenes. It is assumed that the PSF is described by a Gaussian. Instead of the PSF the edge spread function (ESF) was determined. For the investigations RapidEye multispectral orthophoto data (L3a) has been used. Clear transitions between bright and dark patches have been selected in the imagery. The analysis is carried out in a horizontal and vertical direction only. Because of the spectral dependence of the reflection properties, these transitions were examined for all of the five bands of the same image data. This presentation describes a special tool to determine the data quality based on PSF estimations of RapidEye image products. It is also shown that the determination of the influence of different image processing algorithms is possible

Bildqualität von optischen Fernerkundungsdaten

Photogrammetrie und Fernerkundung bieten eine ganze Reihe unterschiedlicher Verfahren zur Ableitung geometrischer, radiometrischer und thematischer Informationen aus Bilddaten. Zur Erfassung der dafür benötigten Bilddaten stehen eine Vielzahl von Flugzeug- und Weltraumsensoren zur Verfügung. Digitale Sensoren bieten auf Grund der Möglichkeiten der absoluten geometrischen und radiometrischen Kalibrierung vielversprechende Möglichkeiten zur Schaffung von Mehrwertprodukten wie digitale Höhenmodelle, Landnutzungskarten etc. Solche Kameras kombinieren die hohe geometrische Qualität mit den radiometrischen Standards von Erdbeobachtungssystemen. Für die Qualitätsbewertung optischer Fernerkundungsdaten sind verschiedene Standards und Spezifikationen verfügbar. Bei der Bestimmung der Bildqualität kann dabei zwischen (spektralen) radiometrischen und geometrischen Aspekten unterschieden werden. Normen enthalten verschiedene Messgrößen für Genauigkeitsprobleme (spektrale, radiometrische und geometrische Genauigkeit) sowie für Leistungsparameter wie SNR, MTF. Bildartefakte sind ein weiteres wichtiges Thema. Der Beitrag führt in die Thematik ein und stellt einen neuen Ansatz zur effizienten radiometrischen Validierung und Kalibrierung operationeller satellitengetragener Fernerkundungssensoren vor

Zur Bewertung von Weltraumkameras

In den letzten Jahren sind eine Reihe von privaten und kommerziellen Anbietern in den Markt für Satellitendaten eingetreten. Auf Grund der Konkurrenzsituation spielt die Bewertung der Güte der Daten und des Sensors eine immer größere Rolle. In dem Beitrag werden mögliche Performanceparameter diskutiert und ein Ansatz für die SNR-Bestimmung nach dem Start auf der Basis natürlicher Targets aus operationellen Bilddaten vorgestellt

Absolute Calibration of a 5 Satellite Constellation Using Vicarious Calibration – 7 Years of Operational Experience

RapidEye is a commercial constellation of 5 satellites carrying identical 5 band multispectral pushbroom imagers. The band combination including the first commercially available Red-Edge band on a multispectral satellites makes the constellation ideal for landuse and landcover applications. These kinds of applications require an accurate absolute calibration of the satellites. To achieve this requirement, the satellites are calibrated using vicarious calibration methods. Since the beginning of operations vicarious calibration techniques using different partners and sites have been used. Vicarious calibration has started using the bright desert sites of Railroad Valley and Ivanpah playa in collaboration with the University of Arizona in 2009. Later the darker 3M site in Brookings South Dakota has been added as a second radiance level with the goal to represent the dynamic range of the sensors better. The proposed presentation is summarizing the campaigns performed over the past years, shows the experiences made with the different sites and demonstrates the results achieved with the vicarious calibration methodology

Inter-Calibration of the RapidEye Sensors with Landsat 8, Sentinel and SPOT

RapidEye is a constellation of 5 identical remote sensing satellites imaging up to 6 mio. sqkm. of the earth surface every day. Among others important application fields of the satellite data are forestry and agriculture. The inter-calibration of the five RapidEye satellites is achieved with a statistical approach supported by vicarious absolute calibration. The data analysis for forestry and agricultural applications sometimes requires an even better temporal resolution of remote sensing data than available from a single satellite system like RapidEye despite it\u27s very high imaging capacity and repetition rate. As the relative spectral response curves of systems like Landsat 8, Sentinel 2, SPOT and RapidEye differ to some extent the data of these different sensors need to be cross calibrated to a common standard before they can be used together within such individual applications. Although cross calibration between different sensors is limited regarding its achievable accuracy due to different reflectance properties of the surface and also due to changing atmospheric conditions, it is a mandatory precondition to minimize potential differences due to sensor effects. This presentation shows the results of the performed analysis and the determined surface dependent spectral band adjustment factors for the different sensors and is additionally showcasing the improvements in a forestry application which was achieved by the application of spectral band adjustment factors

Mapping of grassland using seasonal statistics derived from multi-temporal satellite images

Grasslands cover about 40 % of the earth’s surface. Due to its great expanse and diversity, low-cost tools for inventory, management and monitoring are needed. Remote sensing is a useful technique for providing accurate and reliable information for land use planning and to support large scale grassland management. In the context of “GIO land” (Copernicus initial operations land), which is currently implemented by the European Environment Agency (EEA), the permanent grasslands of 39 countries in Europe has to be mapped with an overall classification accuracy of more than 80 %. Since grassland canopy density, chlorophyll status and ground cover is highly dynamic throughout the growing season, no unique spectral signature can be used to map grasslands. Therefore, it is necessary to use time series to characterize the phenological dynamics of grasslands throughout the year to be able to discriminate among them and other vegetation which shows similar spectral response such as crops. The article outlines the adopted classification method using multi-temporal, multi-scale and multi-source remotely sensed data. The approach is based on the supervised decision Tree (DT) classifier C5 in combination with previous image segmentation and seasonal statistics of bio-physical parameters. In this paper the results of entire Hungary are presented. The accuracy assessment of the grassland classification was carried out using 340 sample points mainly derived from a ground-based European field survey program. The multi-temporal grassland classification of Hungary reached an overall accuracy of 92.2 %

Optimised Near-Real Time Data Acquisition and Pre-processing of Satellite Data for Disaster Related Rapid Mapping

In its first part this paper describes exemplarily optimisations of the satellite systems RapidEye and TerraSAR-X. For this purpose a short insight into processes, relevant for data production, will be given. Focus of this will be time constraints typical for disaster related rapid mapping. Optimisations of geometric pre-processing of satellite data are described in a second part of this paper. For this purpose different software packages available for radar and optical data were compared and analysed respectively. Results were as far as possible optimised