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 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.

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.

Sen2Cor is the atmospheric correction processor selected by ESA for operational, systematic processing of Copernicus Sentinel-2 mission data. It is used for generating the Level-2A products distributed to users by the Copernicus SciHub. Accurate atmospheric correction of Sentinel-2 data and knowledge of its uncertainties are preconditions for high quality downstream applications. In this work we present the comparison of Sentinel-2 Bottom-of-Atmosphere products with measurements of surface reflectance on ground. Source of reference measurements are both surface reflectance data from RadCalNet and from dedicated field campaigns. The analysis shows, that the uncertainty of SR-retrieval with Sen2Cor is better than about 7% for bright surfaces and about 17% for darker. In addition to this performance evaluation, the data are also applied to compare the use of reference data coming from permanent operating bright RadCalNet sites and from ad-hoc field campaigns at darker sites.

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.

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].

Multi-pedestrian tracking in aerial imagery has several applications such as large-scale event monitoring, disaster management, search-and-rescue missions, and as input into predictive crowd dynamic models. Due to the challenges such as the large number and the tiny size of the pedestrians (e.g., 4 × 4 pixels) with their similar appearances as well as different scales and atmospheric conditions of the images with their extremely low frame rates (e.g., 2 fps), current state-of-the-art algorithms including the deep learning-based ones are unable to perform well. In this paper, we propose AerialMPTNet, a novel approach for multi-pedestrian tracking in geo-referenced aerial imagery by fusing appearance features from a Siamese Neural Network, movement predictions from a Long Short-Term Memory, and pedestrian interconnections from a GraphCNN. In addition, to address the lack of diverse aerial pedestrian tracking datasets, we introduce the Aerial Multi-Pedestrian Tracking (AerialMPT) dataset consisting of 307 frames and 44,740 pedestrians annotated. We believe that AerialMPT is the largest and most diverse dataset to this date and will be released publicly. We evaluate AerialMPTNet on AerialMPT and KIT AIS, and benchmark with several state-of-the-art tracking methods. Results indicate that AerialMPTNet significantly outperforms other methods on accuracy and time-efficiency.

This paper describes the contribution of the DLR team ranking 3rdin Track 1 of the 2020 IEEE GRSS Data Fusion Contest, with results ranking 2ndin Track 2 of the same contest being reported in a companion paper. The classifications are based on refinements of low-resolution MODIS labeling using available higher resolution Sentinel-1 and Sentinel-2 data. Results are initialized with a handcrafted decision tree integrating output from a random forest classifier, and subsequently boosted by detectors for specific classes.

This paper describes the contribution of the DLR team ranking 2ndin Track 2 of the 2020 IEEE GRSS Data Fusion Contest. The semantic classification of multimodal earth observation data proposed is based on the refinement of low-resolution MODIS labels, using as auxiliary training data higher resolution labels available for a validation data set. The classification is initialized with a handcrafted decision tree integrating output from a random forest classifier, and subsequently boosted by detectors for specific classes. The results of the team ranking 3rdin Track 1 of the same contest are reported in a companion paper.

Recently developed methods to simulate very high-resolution (VHR) wind fields over complex urban terrain rely on high-quality three-dimensional vector representations of building information. Unfortunately data of that kind is sparsely available on a worldwide scale. In this work, we investigate the applicability of computational fluid dynamics (CFD) on 2.5D digital surface models (DSMs) automatically generated by generative adversarial network (GAN) from globally available satellite data which includes photogrammetric DSMs and pan-chromatic (PAN) images. The obtained results demonstrate that the GAN-based DSMs are reasonable alternatives to rarely available level of detail 2 (LoD2) vector data, promoting large coverage, continuous wind field derivation over complex terrain.

We describe the workflow of a digital surface models (DSMs) refinement algorithm using a hybrid conditional generative adversarial network (cGAN) where the generative part consists of two parallel networks merged at the last stage forming a WNET architecture. The inputs to the so-called WNET-CGAN are stereo DSMs and panchromatic (PAN) half-meter resolution satellite images. Fusing these helps to propagate fine detailed information from a spectral image and complete the missing 3D knowledge from a stereo DSM about building shapes. Besides, it refines the building outlines and edges making them more rectangular and sharp.

Most remote sensing applications require atmospheric correction of satellite images and an increasing part exploits multi-temporal data. Sentinel-2 satellites and Landsat-8 provide almost equivalent satellite images and a joint use of both data sources gives the advantage of a denser time series if the quality of atmospheric correction is consistent. The present study investigates the performance of atmospheric correction processor ATCOR and shows, that it gives consistent results for Sentinel-2 and Landsat-8 data enabling a combined use of both satellites. Both satellite sensors provide the same correct shape of surface reflection spectra.

This paper describes the winning contribution to the 2019 IEEE GRSS Data Fusion Contest Multi-view Semantic Stereo Challenge. In this challenge, a digital surface model (DSM) and a semantic segmentation should be derived from a large number of multi-spectral WorldView-3 images. Results from 50 stereo pairs matched using Semi-Global Matching (SGM) are fused into a DSM. Semantic segmentation is performed with an ensemble of FCN networks taking as input RGB, multi-spectral and height data. Their results are then merged with pixel-wise detectors for the classes water and high vegetation. Compared to the second and third placed teams (mIOU-3 scores of 0.73 and 0.7295), our contribution reached a significantly higher score of 0.745.

The knowledge about the placement and appearance of lane markings is a prerequisite for the creation of maps with high precision, necessary for autonomous driving, infrastructure monitoring, lanewise traffic management, and urban planning. Lane markings are one of the important components of such maps. Lane markings convey the rules of roads to drivers. While these rules are learned by humans, an autonomous driving vehicle should be taught to learn them to localize itself. Therefore, accurate and reliable lane-marking semantic segmentation in the imagery of roads and highways is needed to achieve such goals. We use airborne imagery that can capture a large area in a short period of time by introducing an aerial lane marking data set. In this paper, we propose a symmetric fully convolutional neural network enhanced by wavelet transform in order to automatically carry out lane-marking segmentation in aerial imagery. Due to a heavily unbalanced problem in terms of a number of lane-marking pixels compared with background pixels, we use a customized loss function as well as a new type of data augmentation step. We achieve a high accuracy in pixelwise localization of lane markings compared with the state-of-the-art methods without using the third-party information. In this paper, we introduce the first high-quality data set used within our experiments, which contains a broad range of situations and classes of lane markings representative of today's transportation systems. This data set will be publicly available, and hence, it can be used as the benchmark data set for future algorithms within this domain.

The fusion of heterogeneous image data, in particular optical images and synthetic aperture radar (SAR) images, is highly worthwhile in the context of remote sensing tasks as it allows to exploit complementary information - such as spectral and distance measurements or different observation perspectives - of the two data sources while diminishing their individual weaknesses (e.g. cloud cover, difficulty of image interpretation, limited sensor revisit). However, relating the heterogeneous data on the signal level requires a data alignment step, which cannot be realized without auxiliary knowledge. This paper addresses and discusses this fundamental fusion problem in remote sensing in the context of a framework named SimGeoI, which solves the multi-sensor alignment task based on geometric knowledge from existing digital surface models. Sections of optical and SAR images are related to individual objects using interpretation layers generated with ray tracing techniques. Results of SimGeoI are presented for a test site in London in order to motivate an object-related fusion of remote sensing images.

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.

Significantly outperforming traditional machine learning methods, deep convolutional neural networks have gained increasing popularity in the application of image classification and segmentation. Nevertheless, deep learning-based methods usually require a large amount of training data, which is quite labor-intensive and time-demanding. To deal with the problem in generating training data, we propose in this paper a novel approach to generate image annotations by transferring labels from aerial images to UAV images and refine the annotations using a densely connected CRF model with an embedded naive Bayes classifier. The generated annotations not only present correct semantic labels, but also preserve accurate class boundaries. To validate the utility of these automatic annotations, we deploy them as training data for pixel-wise image segmentation and compare the results with the segmentation using manual annotations. Experiment results demonstrate that the automatic annotations can achieve comparable segmentation accuracy as the manual annotations.

This paper presents the basics and functionality of SimGeoI, a simulation-based framework for the automated interpretation and alignment of optical and SAR remote sensing data. SimGeoI has been developed in order to align optical and SAR data based on given geometric information about objects represented by digital surface models. Thereby, the analysis of urban scenes is possible with independence of sensor type and perspective. After a brief introduction of the processor environment, possible applications of the framework are indicated with results of a case study for Istanbul (WorldView-2 and TerraSAR-X data). In this context, opportunities in the context of a joint analysis of high resolution optical and SAR data are addressed, i.e. concerning data fusion, change detection, and machine learning tasks.

Water vapor is one of the main parameters for the atmospheric correction of Sentinel-2 imagery. Together with the aerosol retrieval, it determines the accuracy of the surface reflectance product. Since Sentinel-2A and soon Sentinel-2B are operational satellites with a free data policy, there is great interest in processing these data and using them for environmental studies. This letter performs a validation of Sentinel-2 retrieved water vapor over land by comparing the scene-derived water vapor column with the Aerosol Robotic Network (AERONET) measurement as an independent source. The validation is performed for a large range of AERONET site elevations and solar zenith angles using the atmospheric precorrected differential absorption technique. Results show a high correlation with a low root-mean-square error of about 0.1 cm for water vapor values from 0.2 to 5 cm.

In this paper, we propose an algorithm for robustly fusing digital surface models (DSMs) with different ground sampling distances and confidences, using explicit surface priors to obtain locally smooth surface models. Robust fusion of the DSMs is achieved by minimizing the L1-distance of each pixel of the solution to each input DSM. This approach is similar to a pixel-wise median, and most outliers are discarded. We further incorporate local planarity assumption as an additional constraint to the optimization problem, thus reducing the noise compared with pixel-wise approaches. The optimization is also inherently able to include weights for the input data, therefore allowing to easily integrate invalid areas, fuse multiresolution DSMs, and to weight the input data. The complete optimization problem is constructed as a variational optimization problem with a convex energy functional, such that the solution is guaranteed to converge toward the global energy minimum. An efficient solver is presented to solve the optimization in reasonable time, e.g., running in real time on standard computer vision camera images. The accuracy of the algorithms and the quality of the resulting fused surface models are evaluated using synthetic data sets and spaceborne data sets from different optical satellite sensors.

RaySAR, a 3D SAR simulator, has been made accessible to the SAR community in January 2016. It helps to understand and analyze reflection effects of radar signals at 3D object models. For instance, the nature of persistent scatterers or “ghost” scatterers at man-made structures or basic reflection effects at canonical shapes can be interpreted in more detail. The decision to provide RaySAR to the community was based on the persisting lack of a freely available SAR simulation package and the repetitive interpretation task related to the analysis of SAR imagery, especially in the context of high resolution data. Many signal reflection effects at objects remain to be discovered and explained. New interfaces between simulation techniques and SAR applications may be helpful. In this context, the paper provides a summary of the status-quo of RaySAR and addresses future directions that have not been followed so far.

Despite the existence of various matching algorithms, matching of images from Micro Aerial Vehicles (MAVs) and airplanes is still a tough problem due to the substantial differences in scale and rotation. This paper investigates the fusion of MAV imagery and airplane imagery and proposes a new robust image matching method with self-adaption to differences in scale and viewing direction. This method is further applied to register a MAV image block with reference to the orthophoto and DSM of a previously-geolocalized aerial image dataset. After registration, a fused 3D point cloud is generated and then combined with images as inputs for land cover (here roofs) classification. Experiments show that the proposed matching method outperforms SIFT/ASIFT methods in both quantity and reliability of matching results, while the registration of MAV imagery achieves decimeter-level accuracy without using any onboard GPS/IMU data. Besides, the pixel-level classification that integrates information of point clouds and images achieves significantly higher accuracy than simply image-based classification.

Multispectral satellite images are often contaminated by haze and/or cirrus. A previous paper presented a haze removal method that calculates a haze thickness map (HTM) based on a local search of dark objects. The haze-free signal is restored by subtracting the HTM from the hazy image assuming an additive model of the haze influence. The HTM method is substantially improved by employing the 1.38-μm cirrus band. The top-of-atmosphere reflectance cirrus band is used as an additional source of information. The method restores the information in highly inhomogeneous surfaces attenuated by a low-altitude haze and high-altitude cirrus, improving the interpretation of the scene content while preserving the shape of the spectral signatures. The new enhanced HTM method is successfully applied to Landsat-8 OLI and Sentinel-2 real and simulated scenes.

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.

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.

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.

The management and protection of grassland biodiversity is of utmost importance as they play a key role in the carbon and hydrological cycle. Therefore, the analysis of their dynamics is of great value given the current ongoing intensification of agricultural land use. To this aim, in this paper we present a novel approach for monitoring grassland dynamics based on polarimetric high-resolution SAR imagery, which is i) capable of handling either dual- or quad-polarization multi-temporal data and ii) supports targeted classification. Results based on dualpol TerraSAR-X as well as dual- and quadpol Radarsat-2 data acquired over a test area in Bavaria (Germany) in 2011 are extremely promising and confirm the effectiveness of the proposed approach.

Due to the recent improvements in satellite sensors and matching technology, the derivation of 3D models from space borne stereo data attached interests in various applications such as urban planning, telecommunication and tourism. Fully automatic 3D building reconstruction from space borne point cloud data is an active research topic, where the relatively low quality of Digital Surface Models (DSM) generated by stereo matching of satellite data comparing to LiDAR data. In order to establish an efficient method to achieve high quality models and complete automation from the mentioned DSM, a new method based on a model-driven strategy is proposed. For improving the results and better detection of building boundaries and corresponding ridgelines, footprints of the buildings and refined ortho-rectified panchromatic images are utilized as additional information. The presented results are promising, at least for larger buildings.

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.

Building change detection is a major issue for urban area monitoring. Due to different imaging conditions and sensor parameters, 2-D information delivered by satellite images from different dates is often not sufficient when dealing with building changes. Moreover, due to the similar spectral characteristics, it is often difficult to distinguish buildings from other man-made constructions, like roads and bridges, during the change detection procedure. Therefore, stereo imagery is of importance to provide the height component which is very helpful in analyzing 3-D building changes. In this paper, we propose a change detection method based on stereo imagery and digital surface models (DSMs) generated with stereo matching methodology and provide a solution by the joint use of height changes and Kullback-Leibler divergence similarity measure between the original images. The Dempster-Shafer fusion theory is adopted to combine these two change indicators to improve the accuracy. In addition, vegetation and shadow classifications are used as no-building change indicators for refining the change detection results. In the end, an object-based building extraction method based on shape features is performed. For evaluation purpose, the proposed method is applied in two test areas, one is in an industrial area in Korea with stereo imagery from the same sensor and the other represents a dense urban area in Germany using stereo imagery from different sensors with different resolutions. Our experimental results confirm the efficiency and high accuracy of the proposed methodology even for different kinds and combinations of stereo images and consequently different DSM qualities.

The goal of this paper is to develop an efficient method for forest change detection using multitemporal stereo panchromatic imagery. Due to the lack of spectral information, it is difficult to extract reliable features for forest change monitoring. Moreover, the forest changes often occur together with other unrelated phenomena, e.g., seasonal changes of land covers such as grass and crops. Therefore, we propose an approach that exploits kernel Minimum Noise Fraction (kMNF) to transform simple change features into high-dimensional feature space. Digital surface models (DSMs) generated from stereo imagery are used to provide information on height difference, which is additionally used to separate forest changes from other land-cover changes. With very few training samples, a change mask is generated with iterated canonical discriminant analysis (ICDA). Two examples are presented to illustrate the approach and demonstrate its efficiency. It is shown that with the same amount of training samples, the proposed method can obtain more accurate change masks compared with algorithms based on k-means, one-class support vector machine, and random forests.

Change detection of two SAR images captured with different incidence angles is a difficult task but may be important in urgent situations like earthquakes. This paper presents a simulation based algorithm to detect negative changes of buildings in two high resolution SAR images captured with different incidence angles. The analysis is supported by LiDAR data where individual wall models are extracted and are simulated to predict their shape in the SAR images. Afterwards, point signatures within the layover areas are extracted, converted to the same geometry, and are compared with a buffer change detection algorithm. The proposed method is tested for several buildings (in Munich city center) imaged in TerraSAR-X spotlight mode.

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.

A solution of difficult tasks in remotely sensed data information extraction can be reached by the development of more complex models. The most important step is in the selection of a relevant and universal methodology for data interpretation, classification, fusion, object detection, etc. Probabilistic graphical models [1] become a more and more popular way for image data annotation and classification [2, 3]. Factor graphs possess important properties such as probabilistic nature, explicit factorization properties, approximate inference, plausible inference of non-full data, easy augmenting, etc., and become relevant for the use in data interpretation systems. In this paper we present several applications of factor graphs for single/multisensory data fusion, classification, and an extension of the graph structure to extract landcover from unseen data. The application of factor graphs allow to obtain an improvement in data fusion/classification accuracy.

Different tasks of multispectral image analysis and processing require specific properties of input pan-sharpened multispectral data such as spectral and spatial consistency. Generally, the quantitative measures for pan-sharpening assessment were taken from other topics of image processing (e.g. image similarity indexes). All these measures are widely employed for this task but the applicability basis of these measures is not checked and proven. In this paper a comparison of pan-sharpening assessment measures for remote sensing is carried out on specially generated pan-sharpened images. Performed statistical analysis on the assessment measures allows to select the measures which are most sensitive to the pan-sharpened imagery quality and these measures are recommended for use.

In the context of global change, alteration of landscapes and loss of biodiversity, the monitoring of habitats, vegetation types and their changes have become extraordinary important. In this paper, first results from a study that analyses the differentiability of NATURA2000 habitats and HNV grassland with imaging radar data are presented. Therefore, Kennaugh elements derived from TerraSAR-X and Radarsat-2 dual pol (VV/VH) time series data are used, both separately and in combination, to model the distribution of these classes with the Maximum-Entropy principle. The preliminary results show that the multi-frequency approach enables - compared to single frequency analyses - a finer differentiation between scatterers in the size of 3-6 cm (e.g. 7120, 7230 and HNV grassland).

Real-time monitoring of crowded regions has crucial importance to avoid overload of people in certain areas. Understanding dynamics of large people crowds can also help to estimate future status of public areas. In order to bring an automatic solution to the problem, herein we introduce four different approaches based on keypoint extraction from airborne images. Using four different keypoint extraction methods separately, we form four different probability density functions (pdf) which hold information about density of people. With our experimental results, we discuss the strengths and weaknesses of these methods in detail. Besides using four different keypoint extraction methods, we also introduce fusion approaches in order to increase the robustness of the algorithm. Our promising experimental results indicate possible usage of the algorithm on real-time on board applications.

Vehicle detection in dense urban areas is often complicated due to car-like objects on rooftops which result in false positive detections. This can be easily avoided by using a digital surface model (DSM) calculated from two consecutive images to exclude those regions. However, in the real-time case traffic information has to be gathered rapidly and the calculation of the DSM for the whole image takes a lot of time. The presented approach suggest a method where the disparity image is only calculated for areas of interest. These areas are selected by projecting the road segments from a road database in the original image using the collinearity equation. The local coordinates of the detected vehicles are then transformed back in the UTM coordinate system using the collinearity equation again. It can be shown that the search area for the detector is significantly reduced and which also leads to improved results of the detection.

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.

The launch of high-resolution remote sensing satellites like TerraSAR-X, WorldView, and Ikonos has benefited the combined application of synthetic aperture radar (SAR) and optical imageries tremendously. Specifically, in case of natural calamities or disasters, decision makers can now easily use an old archived optical with a newly acquired (postdisaster) SAR image. Although the latest satellites provide the end user already georeferenced and orthorectified data products, still, registration differences exist between different data sets. These differences need to be taken care of through quick automated registration techniques before using the images in different applications. Specifically, mutual information (MI) has been utilized for the intricate SAR-optical registration problem. The computation of this metric involves estimating the joint histogram directly from image intensity values, which might have been generated from different sensor geometries and/or modalities (e.g., SAR and optical). Satellites carrying high-resolution remote sensing sensors like TerraSAR-X and Ikonos generate enormous data volume along with fine Earth observation details that might lead to failure of MI to detect correct registration parameters. In this paper, a solely histogram-based method to achieve automatic registration within TerraSAR-X and Ikonos images acquired specifically over urban areas is analyzed. Taking future sensors into a perspective, techniques like compression and segmentation for handling the enormous data volume and incompatible radiometry generated due to different SAR-optical image acquisition characteristics have been rightfully analyzed. The findings indicate that the proposed method is successful in estimating large global shifts followed by a fine refinement of registration parameters for high-resolution images acquired over dense urban areas.

In this paper different methods for deriving digital surface models (DSM) from ALOS Prism three line stereo images are generated and analyzed. The methods used are classical hierarchical stereo matching with forward intersection and two different dense stereo methods. These are digital line warping which was derived from speech recognition algorithms and semi global matching which is originating in computer vision. All these dense stereo methods need epipolar imagery as input and provide so called disparity images as output. For this in a first step the Prism images has to be transformed by pairs to epipolar geometry. For the reprojection of the disparity images to real DSMs rational polynomial coefficients - which were computed from the satellite ephemeris and attitude date - are used. Finally the DSMs generated by all these different methods are compared to a DSM derived from an Ikonos stereo image pair with a ground sampling distance of 1 m.

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.

One of the first essential steps in the analysis of satellite imagery is the orthorectification of the images. Orthorectification without ground control points (GCPs) using only the ephemeris and attitude data provided by the satellite operator provides an absolute accuracy of about 20 m to 1 km (depending on the satellite), which can be improved by measuring precise GCPs. In this paper, a method to obtain GCPs from an existing digital elevation model (DEM) is described and assessed. Since at least the SRTM DEM is available worldwide, DEMs could serve as a valuable additional source for the generation of GCPs. Furthermore, several planned and ongoing missions will increase the availability and accuracy of DEMs or stereo imagery respectively, e.g. ALOS, Tandem-X, etc.

A new approach for the traffic congestion detection in time series of optical digital camera images is proposed. It is well suited to derive various traffic parameters such as vehicle density, average vehicle velocity, beginning and end of congestion, length of congestion or for other traffic monitoring applications. The method is based on the vehicle detection on the road segment by change detection between two images with a short time lag, the usage of a priori information such as road data base, vehicle sizes and road parameters and a simple linear traffic model based on the spacing between vehicles. The estimated velocity profiles for experimental data acquired by airborne optical remote sensing sensor - 3 K camera system - coincide quite well with the reference measurements.

The launch of the advanced high resolution radar satellite TerraSAR-X in summer 2006 opens new possibilities for the demonstration of traffic monitoring from space. DLR is currently developing an operational traffic processor for the TerraSAR-X ground segment. The paper presents results from an airborne SAR GMTI campaign that was part of a study for algorithm development and processor design. DLR’s E-SAR sensor was used in an Along-Track Interferometry (ATI) mode to image vehicles in controlled and uncontrolled situations. The paper gives an overview on the experiment and presents the results of across- and along-track velocity estimation. Adapted SAR processing techniques were applied to enhance the peak energy of the moving objects in the focused SAR images. The paper presents first results and discusses the techniques.

Development and modeling of the road vehicle detection algorithms for SAR images requires the knowledge of the radar cross section of these targets. The radar signatures of the parked cars were derived experimentally from airborne E-SAR image data collected during two flight campaigns. First examples of the catalogue of radar signatures for road vehicles are presented.

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.