On August 2024, the DLR Earth Sensing Imaging Spectrometer (DESIS) completed six years of operations onboard the International Space Station (ISS). In that time, DESIS has acquired data worldwide for both scientific and commercial users. The continuously growing data archive supports methodical and application developments for the monitoring of the Earth’s surface. We present a short update of the mission status and then provide a deeper view into the DESIS data archive. DESIS is currently operating in nominal conditions, further expanding its multitemporal data archive, which holds great value for a wide range of applications and serves as a database for recent and upcoming hyperspectral Earth-observing missions. It enables long-term analysis of physical phenomena and land use changes by providing high-resolution data spanning an extended temporal range for the monitoring of a site of interest.

The retrieval of sun-induced fluorescence (SIF) from hyper-spectral imagery is an ill-posed problem that has been tackled in different ways. We present a novel retrieval method combining semi-supervised deep learning with an existing spectral fitting method. A validation study with in-situ SIF measurements shows high sensitivity of the deep learning method to SIF changes even though systematic shifts deteriorate its absolute prediction accuracy. A detailed analysis of diurnal SIF dynamics and SIF prediction in topographically variable terrain highlights the benefits of this deep learning approach.

The DLR HyperSpectral Unmixing (DLR HySU) open benchmark dataset includes airborne hyperspectral and RGB imagery of targets of different materials and sizes on a homogeneous background, complemented by simultaneous ground-based reflectance measurements. The dataset allows assessing dimensionality estimation, endmember extraction with and without pure pixel assumption, and abundance estimation in the frame of spectral unmixing applications, enabling estimations at sub-pixel level. This paper presents the first works in the literature using the dataset, which demonstrate that DLR HySU is filling a gap regarding validation using real imaging spectrometer data with accurately measured targets.

In many remote sensing applications the measured radiance needs to be corrected for atmospheric effects to study surface properties such as reflectance, temperature or emission features. The correction often applies radiative transfer to simulate atmospheric propagation, a time-consuming step usually done offline. In principle, an efficient machine learning (ML) model can accelerate the simulation step. This is the goal pursued here in the context of solar-induced fluorescence (SIF) emitted by vegetation around the O2-A band using the spaceborne DESIS and airborne HyPlant spectrometers. We present an ML simulator of at-sensor radiances trained on synthetic spectra and describe its performance in detail. The simulator is fast and accurate, constituting a promising alternative to a full-fledged, lengthy radiative transfer code for SIF retrieval in the O2-A band with DESIS and HyPlant.

Since November 2022, the PACO [1] atmospheric correction program has operated routinely as the L2A processor in the ground segment for the hyperspectral missions DESIS [2] and EnMAP [3]. Both missions monitor the Earth’s environment similar to other operational hyperspectral missions like PRISMA [4] and EMIT [5]. The Ground Segment L2A processor for the DESIS and EnMAP missions corrects the at-sensor received terrestrial reflection of the incident solar radiation for the effects of atmospheric constituents generating Bottom-Of-Atmosphere (BOA) ground reflectance spectral image cube, along with pixel-classification masks, Aerosol Optical Thickness (AOT) at 550 nm and Water Vapor (WV) maps. In this contribution we summarize the lessons learned on the validation of the atmospheric correction and the related uncertainty of the hyperspectral L2A products, applying the same validation criteria, for both DESIS and EnMAP.

The DLR Earth Sensing Spectrometer (DESIS) on board the International Space Station (ISS) has been providing high quality hyperspectral data to the scientific community and commercial users since the start of operations in September 2018. After almost 4 years in orbit, the DESIS instrument continues to operate correctly and to deliver hyperspectral data products for a wide variety of applications. In order to support this successful activity, the calibration team regularly analyzes the instrument data and provides updates using vicarious calibration. We present here the latest results from the DES IS vicarious calibration and our plans for future improvements.

The DLR Earth Sensing Imaging Spectrometer (DESIS) [1] is a spaceborne instrument installed and operated on the International Space Station (ISS). The German Aerospace Center (DLR) has developed the instrument and the software for data processing [2], while the US company Teledyne Brown Engineering (TBE) provided the Multi-User System for Earth Sensing (MUSES) platform, where DESIS is installed, and the infrastructure for operations and data tasking [3]. The main parameters of the DESIS instrument are summarized in Table 1. DESIS is equipped with an on-board calibration unit and a rotating pointing mirror (POI). The POI can change the line of sight ±15° in the forward/backward direction (independently of the MUSES orientation), allowing BRDF measurements of the same area on ground within an overflight.

The DLR Earth Sensing Spectrometer (DESIS) on board the International Space Station (ISS) is an imaging spectrometer for remote sensing developed by the German Aerospace Center (DLR) and operated by Teledyne Brown Engineering (TBE). In order to maintain the quality of the data during the operational phase, the calibration team monitors the calibration parameters and updates them when a significant deviation is found. The update of calibration parameters is based on vicarious calibration using Earth scenes over uniform areas and RadCalNet calibration sites. We present here a description of the calibration techniques used for the DESIS instrument with special emphasis on the vicarious calibration.

Soils contain the largest global carbon pool and thus, play an important role in atmospheric CO2 sequestration through increase in soil organic carbon (SOC) stock (Minasny et al., 2017). Therefore, large scale mapping and reporting of soil status, quality and health is starting to get a requirement amongst policy-makers and implemented in target setting of the Sustainable Development Goals and relevant EU policies. Soil quality and health monitoring is commonly described as a sum of physical, chemical and biological properties of soils. Since many years, multispectral and hyperspectral Earth Observation (EO) have been valuable data sources for analyzing the chemical and physical constitution of top soils (Chabrillat et al., 2019). Mainly two approaches are used, the Digital Soil Modelling (DSM) approach as well as the Spectral Soil Modelling (SSM) approach. Both approaches assimilate EO data products such as information regarding the vegetation dynamics and exposed soil reflectance data.

The DLR Earth Sensing Imaging Spectrometer (DESIS) is a space-based instrument installed and operated on the International Space Station (ISS) [1]. This space mission is the achievement of the collaboration between the German Aerospace Center (DLR) and the US company Teledyne Brown Engineering (TBE). DLR has developed the instrument and the software for data processing [2], while TBE provides the Multi-User System for Earth Sensing (MUSES) platform, where DESIS is installed, and the infrastructure for operation and data tasking [3].

Imaging spectrometry provides densely sampled and finely structured spectral information for each image pixel over large areas, enabling the characterization of materials on the Earth's surface by measuring and analyzing quantitative parameters allowing the user to identify and characterize Earth surface materials such as minerals in rocks and soils, vegetation types and stress indicators, and water constituents. The recently launched DLR Earth Sensing Imaging Spectrometer (DESIS) installed on the International Space Station (ISS) closes the long-term gap of sparsely available spaceborne imaging spectrometry data and will be part of the upcoming fleet of such new instruments in orbit. DESIS measures in the spectral range from 400 and 1000 nm with a spectral sampling distance of 2.55 nm and a Full Width Half Maximum (FWHM) of about 3.5 nm. The various DESIS data products available for users are described with the focus on specific processing steps. A summary of the data quality results are given. The product validation studies show that top-of-atmosphere radiance, geometrically corrected, and bottom-of-atmosphere reflectance products meet the mission requirements.

This paper presents a shadow restoration method based on the nonlinear mixture model. A shadowed spectrum is modeled by using a pure sunlit spectrum for the same material following physical assumptions. Regarding pure sunlit and shadowed spectra as endmembers, an unmixing process is then conducted pixel-wise using a nonlinear mixture model. Shadow pixels are restored by simulating their exposure to sunlight through a combination of selected sunlit endmembers spectra, weighted by abundance values. Experiments conducted on a real airborne hyperspectral image are evaluated through spectra comparison and classification. In addition, a soft shadow map is generated, which quantifies the shadow intensity at the edges between sunlit and shadow areas.

DESIS (DLR Earth Sensing Imaging Spectrometer) is a space-based hyperspectral sensor currently installed and operated in the International Space Station (ISS). The instrument is the result of the collaboration between the German Aerospace Center (DLR) and Teledyne Brown Engineering (TBE). DLR has developed the instrument and the software for data processing, while TBE provides the Multi-User System for Earth Sensing (MUSES), where DESIS is installed, and the infrastructure for operation.

This article describes the workflow of the classification algorithm which ranked at 2ndplace in the 2018 GRSS Data Fusion Contest. The objective of the contest was to provide a classification map with 20 classes on a complex urban scenario. The available multi-modal data were acquired from hyperspectral, LiDAR and very high-resolution RGB sensors flown on the same platform over the city of Houston, TX, USA. The classification was obtained by merging deep convolutional and shallow fully-connected neural networks on a simplified set of classes, complemented by a series of specific detectors and ad hoc classifiers.

The German Aerospace Center (DLR) and Teledyne Brown Engineering (TBE), located in Huntsville, Alabama, USA, cooperate to develop and operate the new space-based hyperspectral sensor DLR Earth Sensing Imaging Spectrometer (DESIS). While TBE provides the Multi-User platform MUSES and infrastructure for operation of the DESIS instrument on the ISS, DLR is responsible for providing the instrument and the processing software as well as instrument in-flight calibration and product quality operations. MUSES has been already launched and installed on the International Space Station ISS in early 2017 and DESIS will follow mid of 2018. We present here an overview of the DESIS instrument, the on-ground data processing, the in-flight calibration and product quality investigations.

A major research area in remote sensing is the problem of multi-sensor data fusion. Especially the combination of images acquired by different sensor types, e.g. active and passive, is a difficult task. Over the last years deep learning methods have proven their high potential for remote sensing applications. In this paper we will show how a deep learning method can be valuable for the problem of optical and SAR image matching. We investigate the possible of conditional generative adversarial networks (cGANs) for the generation of artificial templates. Contrary to common template generation approaches for image matching, the generation of templates using cGANs does not require the extraction of features. Our results show the possibility of realistic SAR-like template generation from optical images through cGANs and the potential of these templates for enhancing the matching of optical and SAR images by means of reliability and accuracy.

The Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill the current gap in space-based imaging spectroscopy data. An overview of the main characteristics and current status of the mission will be 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 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 4 days at 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.

The hyperspectral instrument DLR Earth Sensing Imaging Spectrometer (DESIS) will be developed and integrated in the Multi-User-System for Earth Sensing (MUSES) platform installed on the International Space Station (ISS). The DESIS instrument will be launched to the ISS mid of 2017 and installed in one of the four slots of the MUSES platform. The MUSES / DESIS system will be commanded and operated by the publically traded company Teledyne Brown Engineering (TBE), which initiated the program. TBE provides the MUSES platform and the German Aerospace Center (DLR) develops DESIS and establishes a Ground Segment for processing, archiving, delivering and calibrating the data used for scientific and humanitarian applications. Harmonized products will be generated by the Ground Segment established at Teledyne. This article describes the processing ground segment and the foreseen data validation activities. Finally comments regarding the data policy and foreseen scientific uses are given.

Recently, many sparse approximation methods have been applied to solve spectral unmixing problems. These methods in contrast to traditional methods for spectral unmixing are designed to work with large a-prori given spectral dictionaries containing hundreds of labelled material spectra enabling to skip the expensive endmember extraction and labelling step. However, it has been shown that sparse approximation methods sometimes have problems with selection of correct spectra from the dictionary when these are similar. In this paper we study the detection and approximation accuracy of different sparse approximation methods as well as the influence of the proposed modifications.

This study reports the calibration and validation activities for the Environmental Mapping and Analysis Program (EnMAP; www.enmap.org). EnMAP is a German imaging spectroscopy satellite mission with the declared goal to investigate the Earth's surface with a so far surpassing quality. The key scientific questions to which EnMAP will contribute are related to climate change impacts, land cover changes and processes, natural resources, biodiversity and ecosystems, water availability and quality, geohazards and risk management. The satellite operates in a sun synchronous orbit in 650 km height with a local time of the descending node set to 11:00 and an across tilt opportunity to improve the local revisit time. Two pushbroom spectrometers with 242 channels in total cover the spectral range from 420 nm to 2450 nm with a mean resolution of 6.5 nm in the visible and 10 nm in the shortwave-infrared. The ground nadir pixel size is 30 m and 1000 spatial pixels generate a swath with of 30 km. For the CalVal activities, the routine calibration is conducted within the ground segment of DLR, while the independent validation activities are lead by GFZ. Data is operationally processed on-ground to standardized calibrated products and delivered to the international user community [1]. Standardized data products will comprise radiance and reflectance products that make use of calibration information gained pre- and inflight. To ensure high quality standards, additional independent product validation activities are planned.

The standardization of evaluation techniques for building extraction is an unresolved issue in the fields of remote sensing, photogrammetry, and computer vision. In this letter, we propose a metric with a working title “PoLiS metric” to compare two polygons. The PoLiS metric is a positive-definite and symmetric function that satisfies a triangle inequality. It accounts for shape and accuracy differences between the polygons, is straightforward to apply, and requires no thresholds. We show through an example that the PoLiS metric between two polygons changes approximately linearly with respect to small translation, rotation, and scale changes. Furthermore, we compare building polygons extracted from a digital surface model to the reference building polygons by computing PoLiS, Hausdorff, and Chamfer distances. The results show that quantification by the PoLiS distance of the dissimilarity between polygons is consistent with visual perception. Furthermore, Hausdorff and Chamfer distances overrate the dissimilarity when one polygon has more vertices than the other. We propose an approach toward standardizing building extraction evaluation, which may also have broader applications in the field of shape similarity.

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.

An automatic quality assessment of extracted buildings from remote sensing imagery is needed to evaluate extraction algorithms, or to support change detection. In this paper, four commonly used measures are compared to the newly proposed metric for comparison of polygons and line segments (PoLiS). The extracted polygons are compared to the reference polygons and the quality measures are computed for each pair. The symmetric measures, i.e. quality rate and PoLiS, estimate overall dissimilarity between polygons, whereas i.e. the root mean square error (RMSE) of the distances between the polygon vertices, completeness, and correctness, are not symmetric and should be therefore used for applications like change detection. The variability of the measures is assessed according to the area of the reference buildings. The variability is higher for the category of larger buildings, where the building polygon complexity is larger.

Relatively low spatial resolution of the space-borne hyper-spectral images (HSI) is the main drawback to derive value added products. Recently, several techniques have been proposed in order to enhance the spatial resolution HSI by means of fusion with higher spatial resolution multispectral images. This paper presents an alternative approach based on the joint sparsity model for spectral unmixing with the use of a-priori spectral dictionary. To assess the results, we compare our algorithm with the state of the art methods.

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.

EnMAP (Environmental Mapping and Analysis Program; www.enmap.org) is a German, Earth observing, imaging spectroscopy, spaceborne mission planned for launch in 2017. In order to ensure data product standards during the complete mission lifetime operational workflows are established. These cover all activities for pre- and in-flight spectral, radiometric, and geometric characterization and calibration as well as for the independent product validation of the quality controlled images. Spectral and radiometric calibration of the hyperspectral imager covering the wavelength range from 420 nm to 2450 nm is especially based on satellite onboard sources and a full aperture diffuser. Geometric calibration and validation is based on acquisitions of selected reference sites, but also compared to further ground-truth, air-, and spaceborne missions. Standardized products including geometric and/or atmospheric corrections are generated by a fully-automatic hyperspectral image processing chain.

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.

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.

This paper presents a classification methodology for hyperspectral data based on synergetics theory. Pattern recognition algorithms based on synergetics have been applied to images in the spatial domain with limited success in the past, given their dependence on the rotation, shifting, and scaling of the images. These drawbacks can be discarded if such methods are applied to data acquired by a hyperspectral sensor in the spectral domain, as each single spectrum, related to an image element in the hyperspectral scene, can be analyzed independently. The spectrum is first projected in a space spanned by a set of user-defined prototype vectors, which belong to some classes of interest, and then attracted by a final state associated to a prototype. The spectrum can thus be classified, establishing a first attempt at performing a pixel-wise image classification using notions derived from synergetics. As typical synergetics-based systems have the drawback of a rigid training step, we introduce a new procedure which allows the selection of a training area for each class of interest, used to weight the prototype vectors through attention parameters and to produce a more accurate classification map through plurality vote of independent classifications. As each classification is in principle obtained on the basis of a single training sample per class, the proposed technique could be particularly effective in tasks where only a small training data set is available. The results presented are promising and often outperform state-of-the-art classification methodologies, both general and specific to hyperspectral data.

This paper presents a quasi-unsupervised methodology to detect endmembers within an hyperspectral scene and to derive a pixel-wise classification on its basis. The endmember detection step takes as input an overcomplete spectral library, and detects the materials within a scene by analyzing derivative features under the sparsity assumption. The purest pixels for each detected material are then fed to a classifier based on synergetics theory, which is able to produce accurate classification maps on the basis of a restricted training dataset. As the classifier projects the image onto a subspace composed by the classes of interest found in the first step, a focused dimensionality reduction is performed in which every dimension is semantically meaningful.

DLR is operating a generic processing chain for imaging spectrometer data. This includes automatic invocation of system correction, parametric geocoding and atmospheric correction with accompanying quality measurements as well as the archiving of the resulting data products. Further HySpex, a pushbroom scanner, has been purchased in 2011 to be operated from spring 2012. This work describes the steps to be accomplished to incorporate this new sensor into the generic environment.

The future hyperspectral satellite mission EnMAP (Environmental Mapping and Analysis Program; www.enmap.org) will substantially improve remote sensing standard products and generate new user-driven information products on the status and evolution of different ecosystems. The launch is planned for 2016 with mission operations of five years. This paper describes the EnMAP mission and focuses on the status and challenges of how to achieve the required accuracies in geometric correction which applies the method of direct georeferencing. The pre-flight activities including simulations and measurements complement the initial and routine in-flight activities. The concepts for geometric characterization and calibration are analyzed and how thereby the absolute geo-location accuracy and the co-registration between the two spectrometers are realized in the operational on-ground processing. One spectrometer covers the spectral range from 420 nm to 1000 nm and 900 nm to 2450 nm is covered by the other one.

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.

DLR's Remote Sensing Technology Institute has a long lasting experience in developing spaceborne stereo scanners (MEOSS, MOMS) and the corresponding photogrammetric software systems for stereo evaluation and orthorectification. It takes part in the ESA/JAXA-AO Program to evaluate the performance and potential of the three-line stereo scanner PRISM and the multispectral imaging sensor AVNIR-2 on-board the Japanese satellite ALOS as a principal investigator. The high geometric resolution of PRISM (2.5 m ground sampling distance at nadir) combined with the medium swath width of 35 km has the potential to achieve high quality Digital Elevation Models up to 1:25.000 scale topographic maps for various applications. One of the proposed test sites is located near Sana'a, Yemen, where additionally to the PRISM stereo data also an IKONOS stereo image pair exists, which is used for DEM comparison and performance analysis. The results of this test site are evaluated in cooperation with the Advanced Data processing Research INstitute (ADRIN), India and the Federal Institute for Geosciences and Natural Resources (BGR), Hannover.

The very high geometric accuracy of geocoded data of the TerraSAR-X satellite has been shown in several investigations. It is due to the fact that it measures distances which are mainly dependent on the position of the satellite and the terrain height. If the used DEM is of high accuracy, the resulting geocoded data are very precise. This precision can be used to improve the exterior orientation and thereby the geometric accuracy of optical satellite data. The technique used is the measurement of identical points in the images, either by manual measurements or through local image matching using mutual information and to estimate improvements for the attitude data through this information. By adjustment calculations falsely matched points can be eliminated and an optimal improvement can be found. The optical data are orthorectified using these improvements and the available DEM. The results are compared using conventional ground control information from GPS measurements.

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.

The German along-track stereo scanner MOMS-2P delivered 3-line stereo imagery of many parts in the world from 1996 till 1999. Its satellite platform was the Russian space station Mir. The relative accuracy of the exterior orientation of the MOMS camera based upon GPS and gyro measurements (1 m and 1-2 arcseconds, respectively) was sufficient with respect to MOMS ground pixel size of 17 m. In order to get the absolute orientation, bias and drift parameters and some values of the interior orientation of the camera have to be estimated via bundle adjustment. For this task, ground control points (GCP) have to be provided. For large areas in Afghanistan, Jordan, and Libya, geocoded thematic mapper imagery was available. Automated image matching is used to extract thousands of GCP even though the time gap between Landsat and MOMS imaging ranges from 8 to 11 years. The geoid height of the GCP is taken from available digital elevation models (DEM) of lower accuracy. The GCP enter a bundle adjustment which results in improved values of exterior and interior orientation. These are then used in DEM derivation and orthoimage production for the three MOMS viewing directions. DLR's MOMS stereo workstation software is used for all these tasks. The fit between TM and MOMS orthoimages and between the orthoimages of the off-nadir looking MOMS channels is checked via automated image matching. Mean and standard deviations of the shifts are found to be in sub-pixel range.

Sensor viewing angle effects, which are caused mainly by an atmosphere and a sun-sensor-target geometry, are observed quite often in images acquired by optical remote sensing sensors, especially airborne sensors with a wide field of view. We propose an image-based empirical radiometric normalization method, which is based on a linear regression applied over linear models between the observed radiance and the target radiance for each surface class separately. The experiments for data acquired by airborne multispectral scanner DAEDALUS AADS 1268 ATM show the effectiveness and potential of the proposed method especially for the mosaicking and classification applications.

Recent remote sensing applications are moving from an interpretation of single image swaths to regional mosaics. Within-swath and between-swath radiometric variations and ortho-rectification errors, especially for airborne sensors with a wide field of view, cause most of the problems during the mosaicking process. The proposed procedure for mosaicking consists of the following three steps. Within-swath radiometric normalization is performed using an image-based empirical radiometric correction method, which accounts for sensor viewing angle effects. Individual swaths are ortho-rectified using a direct geo-referencing approach. To remove the between-swath radiometric variations we propose to use the radiometric correction method, which is based on the information contained in the overlapping region of the swaths. The experiments for data acquired by airborne multi-spectral scanner DAEDALUS AADS 1268 ATM show the effectiveness and potential of the proposed method especially for the thematic analysis applications.