Rupert Müller

The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

Imaging spectroscopy, also known as hyperspectral remote sensing, is based on the characterization of Earth surface materials and processes through spectrally-resolved measurements of the light interacting with matter. The potential of imaging spectroscopy for Earth remote sensing has been demonstrated since the 1980s. However, most of the developments and applications in imaging spectroscopy have largely relied on airborne spectrometers, as the amount and quality of space-based imaging spectroscopy data remain relatively low to date. The upcoming Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill this gap. An overview of the main characteristics and current status of the mission is provided in this contribution. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible and near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km-wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to four days at the Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide.

Adaptive Shadow Detection Using a Blackbody Radiator Model

The application potential of remotely sensed optical imagery is boosted through the increase in spatial resolution, and new analysis, interpretation, classification, and change detection methods are developed. Together with all the advantages, shadows are more present in such images, particularly in urban areas. This may lead to errors during data processing. The task of automatic shadow detection is still a current research topic. Since image acquisition is influenced by many factors such as sensor type, sun elevation and acquisition time, geographical coordinates of the scene, conditions and contents of the atmosphere, etc., the acquired imagery has highly varying intensity and spectral characteristics. The variance of these characteristics often leads to errors, using standard shadow detection methods. Moreover, for some scenes, these methods are inapplicable. In this paper, we present an alternative robust method for shadow detection. The method is based on the physical properties of a blackbody radiator. Instead of static methods, this method adaptively calculates the parameters for a particular scene and allows one to work with many different sensors and images obtained with different illumination conditions. Experimental assessment illustrates significant improvement for shadow detection on typical multispectral sensors in comparison to other shadow detection methods. Examples, as well as quantitative assessment of the results, are presented for Landsat-7 Enhanced Thematic Mapper Plus, IKONOS, WorldView-2, and the German Aerospace Center (DLR) 3K Camera airborne system.

Noise Reduction in Hyperspectral Images Through Spectral Unmixing

Spectral unmixing and denoising of hyperspectral images have always been regarded as separate problems. By considering the physical properties of a mixed spectrum, this letter introduces unmixing-based denoising, a supervised methodology representing any pixel as a linear combination of reference spectra in a hyperspectral scene. Such spectra are related to some classes of interest, and exhibit negligible noise influences, as they are averaged over areas for which ground truth is available. After the unmixing process, the residual vector is mostly composed by the contributions of uninteresting materials, unwanted atmospheric influences and sensor-induced noise, and is thus ignored in the reconstruction of each spectrum. The proposed method, in spite of its simplicity, is able to remove noise effectively for spectral bands with both low and high signal-to-noise ratio. Experiments show that this method could be used to retrieve spectral information from corrupted bands, such as the ones placed at the edge between ultraviolet and visible light frequencies, which are usually discarded in practical applications. The proposed method achieves better results in terms of visual quality in comparison to competitors, if the mean squared error is kept constant. This leads to questioning the validity of mean squared error as a predictor for image quality in remote sensing applications.

Automated Georeferencing of Optical Satellite Data with Integrated Sensor Model Improvement

The geometric processing of remotely sensed image data is one of the key issues in data interpretation, added value product generation, and multi-source data integration. Although optical satellite data can be orthorectified without the use of Ground Control Points (GCP) to absolute geometric accuracies of some meters up to several hundred meters depending on the satellite mission, there is still a need to improve the geometric accuracy by using GCP. The manual measurement of GCP is time consuming work, and leads, especially for larger data sets with hundreds of satellite images, to a cost and time ineffective workload. To overcome these shortcomings, an autonomous processing chain to georeference and orthorectify optical satellite data is proposed which uses reference data and digital elevation models to generate GCP and to improve sensor model parameters (namely for rigorous and universal sensor models) for a series of optical Earth observation satellite systems. Using a restrictive blunder removal strategy, the proposed procedure leads to high quality orthorectified products or at least to a geometrically consistent data set in terms of relative accuracy. The geometric processing chain is validated using SPOT-4 HRVIR, SPOT-5 HRG, IRS-P6 LISS III, and ALOS AVNIR-2 optical sensor data, for which a huge amount of satellite data (3,200 scenes) has been processed. Relative and absolute geometric accuracies of approximately half the pixel size (linear Root Mean Square Error) are achieved.

Processors for ALOS Optical Data: Deconvolution, DEM Generation, Orthorectification, and Atmospheric Correction

The German Aerospace Center (DLR) is responsible for the development of prototype processors for PRISM and AVNIR-2 data under a contract of the European Space Agency. The PRISM processor comprises the radiometric correction, an optional deconvolution to improve image quality, the generation of a digital elevation model, and orthorectification. The AVNIR-2 processor comprises radiometric correction, orthorectification, and atmospheric correction over land. Here, we present the methodologies applied during these processing steps as well as the results achieved using the processors.

In-flight Geometric Calibration and Orientation of ALOS/PRISM Imagery with a Generic Sensor Model

Self-calibration is a powerful technique to exploit the geometric potential of optical spaceborne sensors. This paper explains the methodology of expanding a sensor model for in-orbit geometric calibration of the PRISM radiometers on the Japanese ALOS satellite. PRISM has three optical systems for forward, nadir, and backward views each with a 2.5 m nominal spatial resolution. Algorithms for the geometric processing of the PRISM images are proposed and implemented. It is shown how self calibration and orientation of the sensor can be done without having precise knowledge of the payload geometry and attitude data. Several cases and procedures are studied with the established sensor model, including weight matrices, attitude offsets, attitude drifts, and focal length estimations. It is concluded that self calibration of the PRISM cameras can be done effectively with a rigorous sensor model. Even if the post-launch parameters are not available, sub-pixel geometric accuracy can be achieved.

Haze Detection and Removal in Remotely Sensed Multispectral Imagery

Haze degrades optical data and reduces the accuracy of data interpretation. Haze detection and removal is a challenging and important task for optical multispectral data correction. This paper presents an empirical and automatic method for inhomogeneous haze detection and removal in medium- and high-resolution satellite optical multispectral images. The dark-object subtraction method is further developed to calculate a haze thickness map, allowing a spectrally consistent haze removal on calibrated and uncalibrated satellite multispectral data. Rare scenes with a uniform and highly reflecting landcover result in limitations of the method. Evaluation on hazy multispectral data (Landsat 8 OLI and WorldView-2) and a comparison to haze-free reference data illustrate the spectral consistency after haze removal.

Towards traffic monitoring with TerraSAR-X

This article presents an overview of the traffic monitoring project initiated in the context of the TerraSAR-X mission and the German Aerospace Center (DLR) transportation research program. A short description of the TerraSAR-X instrument is presented, followed by an analysis of the necessary TerraSAR-X system settings for traffic monitoring and an assessment of the expected image quality. Based on a brief revision of the theory of moving object effects in synthetic aperture radar (SAR) images and a quantification of these effects for the TerraSAR-X case, an outline is presented of the processing system that is currently implemented to detect moving vehicles and estimate their velocities. Special emphasis is placed on the integration of a priori information derived from road databases for improving detection rates and velocity estimation accuracy.

Joint Sparsity Model for Multilook Hyperspectral Image Unmixing

Recent work on hyperspectral image (HSI) unmixing has addressed the use of overcomplete dictionaries by employing sparse models. In essence, this approach exploits the fact that HSI pixels can be associated with a small number of constituent pure materials. However, unlike traditional least-squares-based methods, sparsity-based techniques do not require a preselection of endmembers and are thus able to simultaneously estimate the underlying active materials along with their respective abundances. In addition, this perspective has been extended so as to exploit the spatial homogeneity of abundance vectors. As a result, these techniques have been reported to provide improved estimation accuracy. In this letter, we present an alternative approach that is able to relax, yet exploit, the assumption of spatial homogeneity by introducing a model that captures both similarities and differences between neighboring abundances. In order to validate this approach, we analyze our model using simulated as well as real hyperspectral data acquired by the HyMap sensor.

Unmixing-based Denoising for Destriping and Inpainting of Hyperspectral Images

Unmixing-based Denoising exploits spectral unmixing results to selectively recover bands affected by a low Signal-to-Noise Ratio in hypespectral images. This paper proposes to apply this algorithm, which operates pixelwise, for the inpainting of corrupted pixels and the removal of drop-out artifacts in hy-perspectral scenes. The reported experiments are characterized by a low reconstruction error for the reconstructed spectra and a high visual quality of the processed images, and outperform state of the art methods in terms of reconstruction error.