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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.

This paper presents a planning framework for discrete planning of human operations in highly automated industrial plants, where manual and automatic control coexist. Based solely on qualitative knowledge of the controlled continuous process represented through signed directed graphs, the planner exploits a greedy algorithm to determine the optimal action for the human operator upon changes in the process’ operating conditions or reconfiguration of the control system due to faulty conditions. The planner results in a decision support tool that instructs the human operator on the best course of action. The planning framework and the resulting planner are demonstrated on a quadruple-tank system.

This paper describes the contribution of the DLR team ranking 2nd in 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 3rd in Track 1 of the same contest are reported in a companion paper.

This paper describes the contribution of the DLR team ranking 3rd in Track 1 of the 2020 IEEE GRSS Data Fusion Contest, with results ranking 2nd in 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 proposes a new approach for building change detection using multi-temporal satellite stereo data. This approach is composed of three main steps. Firstly the building probability map can be derived by a state-of-the-art deep learning approach. In the second step, a decision fusion based fusion model refines and fuses the building changes from satellite stereo imagery and the digital surface models (DSMs). In the last step, the building probability maps are further fused with the building change indicators to generate an improved change detection result. Experiments on the multi-temporal data acquired over 5 years confirms the effectiveness of the proposed approach.

Building semantic segmentation is a crucial task for building information modeling (BIM). Current research generally exploits terrestrial image data, which provides only limited view of a building. By contrast, oblique imagery acquired by unmanned aerial vehicle (UAV) can provide richer information of both the building and its surroundings at a larger scale. In this paper, we present a novel pipeline for building semantic segmentation from oblique UAV images using a fully convolutional neural network (FCN). To cope with the lack of UAV image annotations at facade level, we leverage existing ground-view facades databases to simulate various aerial-view images based on estimated homography, yielding abundant synthetic aerial image annotations as training data. The FCN is trained end-to-end and tested on full-tile UAV images. Experiments demonstrate that the incorporation of simulated views can significantly boost the prediction accuracy of the network on UAV images and achieve reasonable segmentation performance.

Tree species information is crucial in sectors such as forest management and nature conservation. It is often required over a large area. In this study, tree species classification was performed using hyperspectral data and the Digital Surface Model generated from DLR-3K aerial borne stereo camera System. In the classification step, pixel-based approach and the patch-based approach with Bag-of-Word (BoW) model were proposed and tested. The two approaches have been performed in the Kranzberg Forest near Munich, Germany. The comparison was taken in a statistical way. By using proper features combination, the pixel-based classification can achieve very high accuracy (Kappa $=0.95)$, while the patch-based method only has accuracy around 60%.

Tree species information is crucial in sectors such as forest management and nature conservation. It is often required over a large area. In this study, tree species classification was performed using hyperspectral data and the Digital Surface Model generated from DLR-3K aerial borne stereo camera System. In the classification step, pixelbased approach and the patch-based approach with Bag-ofWord (BoW) model were proposed and tested. The two approaches have been performed in the Kranzberg Forest near Munich, Germany. The comparison was taken in a statistical way. By using proper features combination, the pixel-based classification can achieve very high accuracy (Kappa =0.95), while the patch-based method only has accuracy around 60%.

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.

In this paper, we propose a Bayesian linear regression method for person density estimation in extremely crowded areas in aerial images. The fundamental idea is to learn a mapping function from local features to crowd density. In order to describe the appearances of persons within a crowd in aerial images, local texture features are computed for each small local neighborhood. Then we cast the problem as a linear regression. In order to model the nonlinearity between local features and crowd density, Gaussian basis functions are used and their locations are determined by a k-means clustering. Crowd density can be estimated by Bayesian inference. However, due to the presence of a hyper-prior distribution, variational inference is applied to compute the predictive distribution. Through experiments, the effectiveness of the proposed method for crowd density estimation is demonstrated.

Within the German Plant Phenotyping Network (DPPN), we developed a novel PET scanner based on Philips Digital Photon Counters (DPCs, or dSiPMs = digital Silicon Photomultipliers). The scanner is dedicated for plant research and provides functional information on carbon transport within the plant. To this end the detector ring is oriented horizontally. It provides a Field-of-View of 18 cm dia. and 20 cm in height. The read-out electronics cluster hits from different photodetector pixels when they originate from the same scintillation event. These single events are written via USB 3.0 with up to 300 MB/s to the computer system. Crystal identification, energy discrimination and coincidence detection is realized in software. The spatial resolution in the center Field-of-View (CFOV) could be estimated to approx. 1.6 mm from measurements of a dedicated hot rod phantom. Preliminary sensitivity measurements result in a peak sensitivity of 4.04% (ΔE = 250-750 keV) in the CFOV and a Coincidence Resolving Time of 298 ps could be achieved.

We extend our system for radio astronomical monitoring by a cross-validation filter, separating near Earth radio frequency interference (RFI) from deep space signals. The filter searches for similar signals in a nearby frequency band, coming from a different spatial direction than the tested signal. The filter passes the signals which do not have such duplicates. We apply this technique to a database of SETI Institute (setilive.org), containing 1.5 millions of sky observations in a frequency range 0.5-11.2~GHz, where our primary selection identified 28 strong signals possessing extraterrestrial (ET) signature. Cross-validation allows to filter out 24 of those signals as satellite RFI. We present parameters for the remaining 4 signals and discuss statistical significance of these findings.

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.

Digital Surface Models (DSMs) generated from satellite stereo imagery provide valuable but not comprehensive information for building change detection. Therefore, belief functions have been introduced to solve this problem by fusing DSM information with changes extracted from images. However, miss-detection can not be avoided if the DSMs are containing large region of wrong height values. A refined workflow is thereby proposed by adopting the initial disparity map to generate a reliability map. This reliability map is then built in the fusion model. The reliability map has been tested in both Dempster-Shafer Theory (DST), and Dezert-Smarandache Theory (DSmT) frameworks. The results have been validated by comparing to the manually extracted change reference mask.

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.

In this paper we apply the Multi-Look Joint Sparsity Fusion algorithm to multisensor image data. Our algorithm at first performs sparse unmixing of the hyperspectral data and selects pixels for a second unmixing of the multispectral image. This is done by applying a joint sparsity model, which exploits similarities within neighbouring pixels. We test our resolution enhancement method using a hyperspectral and a multispectral image with a spatial resolution of 30 m and 3 m, respectively. To asses the results we evaluate the classification result of the resolution enhanced and original images.

In the framework of the German Plant Phenotyping Network (DPPN) we developed a novel PET scanner for imaging plants and crops. The observation of the carbon transport within the plant becomes possible by using 11CO2 as PET tracer. The use of the rather short living isotope C-11 asks for a scanner with high dynamic range. That means fast timing and high data rates are important features which let us choose the Philips Digital Photon Counter (DPC) as photo detector. Due to the fast photo detectors and the special crystal matrix arrangement the system will allow measurements with rather high activities. We could measure a coincidence resolution time of ∼ 250 ps FWHM between two detector elements. This opens the opportunity to employ time-of-flight information for the first time on a PET scanner of this size. This paper presents very first results from a prototype single-ring system with a FOV of 18 cm diameter and 6.5 cm axial height.

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.