Christian Fischer

A data-driven approach to quality assessment for hyperspectral systems

The increasing use of products based on airborne hyperspectral data for decision-making calls for a thorough quality assessment. Due to the complexity of the corresponding processing chain, as well as the variety of physical processes involved, such a task is usually only performed in specific cases and on specific parts of the processing chain. In particular, the quality assessment of data-products is still an open issue. A generic quality assessment method - based on an cross-comparison of errors - is proposed in this paper.Airborne hyperspectral - also called imaging spectroscopy - data is commonly acquired by means of whisk- or push-broom sensors, and requires several strips - or flight-lines - to cover the full area of interest. A comparison of the discrepancies between overlapping parts of these flight-lines is used to retrieve an assessment of the measurement reproducibility. This mapping can be performed on pre-processed data which avoids the need to separately investigate all input parameters and their associated models, hence bypassing the curse of dimensionality.The first step involves retrieving the pairs of pixels corresponding to the same areas imaged from overlapping flight-lines. Even when an ortho-rectification of the data has been carried out, various phenomena, such as errors in the underlying digital elevation model, lead to flight-line mis-registrations. For heterogeneous land-covers, a pixel to pixel registration step has therefore to be performed in order to allow a cross-comparison of pixels: a suitable methodology is proposed along with its validation. The second step corresponds to the relative errors analysis itself. A set of quantitative quality indicators - corresponding to different types of land-products - is presented. These methods are illustrated with an example along with a discussion.This approach can be used on any reasonably well contrasted scene to retrieve a quality assessment for any raster product independently of its data type as well as for the reflectance data itself. HighlightsIt allows a quality assessment over the whole processing chain, hence including image as well as mapping uncertainties.It is generic and can be performed on archived data without additional information.It requires limited human interaction and can therefore easily be incorporated in a processing chain.It presents a build in co-registration method for dealing with heterogeneous data.

Atmospheric correction of 2000 HyMAP data in the framework of the EU-project MINEO

During the MINEO flight-campaign in summer 2000 HyMap data was recorded for the test site Kirchheller Heide north of the Ruhr district. The aim of this project is to use hyperspectral spectrometer data to detect environmental and ecological changes. They are caused by an affected hydrological balance due to deep hard coal mining. Dynamic mining demands regular updates of the spatial information. This data will become part of an environmental monitoring system which shall comprise analysis of hyperspectral data as an important constituent. As an essential preprocessing step for vegetation studies and multitemporal analyses, ATCOR-4 was used for atmospheric correction. Additionally, atmospheric data from Deutscher Wetterdienst (DWD) and ground reflectance spectra were recorded as an essential data-input for the ATCOR 4 model. The field spectra were used first to control the accuracy of the standard calibration files which are provided with the hyperspectral data. This workflow provides accurate results for wavelengths shorter than 1 micrometers directly. In an interactive manner the inflight calibration module of ATCOR-4 allows to built up and adapt new calibration files suitable for all wavelengths. The first calculated atmospheric lookup-tables and this final calibration file were used to perform the atmospheric correction for the HyMap scene.

Design and Implementation of Data Usability Processor into an Automated Processing Chain for Optical Remote Sensing Data

Diverse anthropogenic impacts will trigger worldwide environmental and social problems as e.g. climate change or social transformation processes. To observe these processes current information about status, direction of development and spatial or temporal dynamics of the processes are required. As the demand for current environmental information is increasing, earth observation (EO) and remote sensing (RS) techniques are moving to the focus of interest.

Experiences of coal fire detection and quantification for resources management

Uncontrolled coal seam fires are an environmental and economic problem of international magnitude. Today, China is the leading country of coal production, consumption and export. The annual Chinese coal production, which is about approx. 2.4 Mt per year, faces a problem of numerous uncontrolled burning coal fires mostly located in the northern Chinese coal belt. The determination of fire radiative energy (FRE) has been introduced recently as a new remote sensing technique to quantify forest and grassland fires. The main research in this project focuses on the development of robust routines to detect coal fire related thermal anomalies within the different areas under investigation. These developments are the prerequisite for a coal fire monitoring.

Monitoring of the 2015 Villarrica Volcano Eruption by Means of DLR’s Experimental TET-1 Satellite

Villarrica Volcano is one of the most active volcanoes in the South Andes Volcanic Zone. This article presents the results of a monitoring of the time before and after the 3 March 2015 eruption by analyzing nine satellite images acquired by the Technology Experiment Carrier-1 (TET-1), a small experimental German Aerospace Center (DLR) satellite. An atmospheric correction of the TET-1 data is presented, based on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Database (GDEM) and Moderate Resolution Imaging Spectroradiometer (MODIS) water vapor data with the shortest temporal baseline to the TET-1 acquisitions. Next, the temperature, area coverage, and radiant power of the detected thermal hotspots were derived at nsubpixel level and compared with observations derived from MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) data. Thermal anomalies were detected nine days before the eruption. After the decrease of the radiant power following the 3 March 2015 eruption, a stronger increase of the radiant power was observed on 25 April 2015. In addition, we show that the eruption-related ash ncoverage of the glacier at Villarrica Volcano could clearly be detected in TET-1 imagery. Landsat-8 imagery was analyzed for comparison. The information extracted from the TET-1 thermal data is thought be used in future to support and complement ground-based observations of active volcanoes.

High Temperature Fire Experiment for TET-1 and Landsat 8 in Test Site DEMMIN (Germany)

In 2012, the German Aerospace Center (DLR) launched the small satellite TET-1 (Experimental Technology Carrier) as a test platform for new satellite technologies and as a carrier for the Multi-Spectral Camera System (MSC) with five spectral bands (Green, Red, Near Infrared, Middle Infrared, and Thermal Infrared). The MSC has been designed to provide quantitative parameters (e.g. fire radiative power, burned area) observing high-temperature events. The detection of such events provides information for operational support to fire brigades, to change detection of hotspots, to assess CO2 emissions of burning vegetation, and, finally, contributes to the monitoring programs that support climate models. In order to investigate the sensitivity and accuracy of the MSC system, a calibration and validation fire campaign was developed and executed, to derive characteristic signal changes of corresponding pixels in the MWIR and LWIR bands. The planning and execution of the validation campaign and the results are presented.

Reliability assessment for remote sensing data: Beyond Cohen's kappa

Cohens κ coefficient has been widely used for assessing classification results derived from remote sensing data. It however presents several limitations, which are preventing both an efficient use as well as a generalisation of its use. This paper reviews these problems and proposes as an alternative to prefer the Krippendorffs α-coefficient over Cohens κ. Krippen-dorffs α indeed presents less flaws while dealing with more data types - hence allowing the rating of quantitative data -and managing the case where more than two judgements are issued and finally dealing with cases where no judgement is issued. These concepts are be illustrated by some exemplary data-sets.

Corresponding pixel automated matcher (CPAM) and application to analysis of hyperspectral data variability

In order to produce validated, and therefore reliable, products based on hyperspectral imagery, it is necessary to first characterise the data quality itself. Since hyperspectral airborne imagery is usually acquired in several partially overlapping flight-lines [1], one can use this informationredundancy to retrieve the imagery uncertainty. In this paper, a generic algorithm, the corresponding pixel automated matcher (CPAM) allowing to extract pixels from two overlapping flight-lines corresponding to the same geo-location is proposed. In a second time, these results are used to obtain a quantification of the imagery internal variability as well as for producing quality indicators for land-products.