Anko Börner

SENSOR: a tool for the simulation of hyperspectral remote sensing systems

Abstract The consistent end-to-end simulation of airborne and spaceborne earth remote sensing systems is an important task, and sometimes the only way for the adaptation and optimisation of a sensor and its observation conditions, the choice and test of algorithms for data processing, error estimation and the evaluation of the capabilities of the whole sensor system. The presented software simulator SENSOR (Software Environment for the Simulation of Optical Remote sensing systems) includes a full model of the sensor hardware, the observed scene, and the atmosphere in between. The simulator consists of three parts. The first part describes the geometrical relations between scene, sun, and the remote sensing system using a ray-tracing algorithm. The second part of the simulation environment considers the radiometry. It calculates the at-sensor radiance using a pre-calculated multidimensional lookup-table taking the atmospheric influence on the radiation into account. The third part consists of an optical and an electronic sensor model for the generation of digital images. Using SENSOR for an optimisation requires the additional application of task-specific data processing algorithms. The principle of the end-to-end-simulation approach is explained, all relevant concepts of SENSOR are discussed, and first examples of its use are given. The verification of SENSOR is demonstrated. This work is closely related to the Airborne PRISM Experiment (APEX), an airborne imaging spectrometer funded by the European Space Agency.

OBC-NG: Towards a reconfigurable on-board computing architecture for spacecraft

The computational demands on spacecraft are rapidly increasing. Current on-board computing components and architectures cannot keep up with the growing requirements. Only a small selection of space-qualified processors and FPGAs are available and current architectures stick with the inflexible cold-redundant structure. The objective of the ongoing project OBC-NG (On-board Computer - Next Generation) is to find new concepts for on-board-computer to fulfill future requirements. The concept presented in this paper is based on a distributed reconfigurable system, consisting of different nodes for processing, management and interface operations. OBC-NG will exploit the high performance of commercial off-the-shelf (COTS) hardware parts. To compensate the shortcomings of COTS parts the OBC-NG redundancy approach differs from the classic way and error mitigation techniques will work mainly on software level. This paper discusses the hardware and software architecture of the system as well as the redundancy and reconfiguration concept. Our ideas will be proven in an OBC-NG prototype, planned for the next year.

Multimodal retinal image registration using a fast principal component analysis hybrid-based similarity measure

Multimodal retinal images (RI) are extensively used for analysing various eye diseases and conditions such as myopia and diabetic retinopathy. The incorporation of either two or more RI modalities provides complementary structure information in the presence of non-uniform illumination and low-contrast homogeneous regions. It also presents significant challenges for retinal image registration (RIR). This paper investigates how the Expectation Maximization for Principal Component Analysis with Mutual Information (EMPCA-MI) algorithm can effectively achieve multimodal RIR. This iterative hybrid-based similarity measure combines spatial features with mutual information to provide enhanced registration without recourse to either segmentation or feature extraction. Experimental results for clinical multimodal RI datasets comprising colour fundus and scanning laser ophthalmoscope images confirm EMPCA-MI is able to consistently afford superior numerical and qualitative registration performance compared with existing RIR techniques, such as the bifurcation structures method.

Robust retinal image registration using expectation maximisation with mutual information

Retinal images (RI) are widely used to diagnose a variety of eye conditions and diseases such as myopia and diabetic retinopathy. They are inherently characterised by having nonuniform illumination and low-contrast homogeneous regions which represent a unique set of challenges for retinal image registration (RIR). This paper investigates using the expectation maximization for principal component analysis based mutual information (EMPCA-MI) algorithm in RIR. It combines spatial features with mutual information to efficiently achieve improved registration performance. Experimental results for mono-modal RI datasets verify that EMPCA-MI together with Powell-Brent optimization affords superior robustness in comparison with existing RIR methods, including the geometrical features method.

On Design and Applications of Cylindrical Panoramas

The paper briefly overviews the design and applications of cylindrical panoramic cameras characterized by a rotating linear sensor capturing one image column at time. The camera provides very high image resolutions paid by motion distortions in dynamic scenes. The images are used for stereo reconstruction and visualization of static scenes when extremely high image resolution is of benefit.

Mid-level segmentation and segment tracking for long-range stereo analysis

This paper presents a novel way of combining dense stereo and motion analysis for the purpose of mid-level scene segmentation and object tracking. The input is video data that addresses long-range stereo analysis, as typical when recording traffic scenes from a mobile platform. The task is to identify shapes of traffic-relevant objects without aiming at object classification at the considered stage. We analyse disparity dynamics in recorded scenes for solving this task. Statistical shape models are generated over subsequent frames. Shape correspondences are established by using a similarity measure based on set theory. The motion of detected shapes (frame to frame) is compensated by using a dense motion field as produced by a real-time optical flow algorithm. Experimental results show the quality of the proposed method which is fairly simple to implement.

Advanced sensors for surveying and mapping

During the last years the department of Optical Information Systems of the German Aerospace Center (DLR) developed a considerable number of imaging sensor systems for a wide field of applications. Systems with a high geometric and radiometric resolution in dedicated spectral ranges of the electromagnetic spectrum were provided by developing and applying cutting edge technologies. Designed for photogrammetry and remote sensing, such systems play an important role for security and defence tasks. Complete system solutions were implemented considering theoretical framework, hardware design and deployment, overall system tests, calibration, sensor operation and data processing. Outstanding results were achieved with the airborne digital sensor ADS40 and the micro satellite BIRD and its infrared camera payload. Future activities will focus on intelligent cameras and sensor webs. The huge amount of data will force the issue of thematic multi-sensor data processing which is to be implemented in real time near the sensor. In dependence on well defined tasks, combinations of several sensors with special properties will be placed on spaceborne, airborne or terrestrial platforms. The paper gives an overview about finished and current projects and strategic goals.

Results of Test Flights with the Airborne Digital Sensor ADS40

During the past two years the company LH Systems and the German Aerospace Center (DLR) have developed the commercial airborne digital sensor ADS40 based on the three-line principle. By assembling additional CCD lines into the same focal plane, the sensor is capable of generating a number of color images. In the first part, the sensor system itself is introduced shortly. The main concept and the key features are described and an overview of the data processing scheme is given. After that, we will focus on the results of test flights. The emphasis is placed on the properties of the overall system including the sensor itself, platform, airplane, and inertial measurement unit. The effect of using staggered CCD lines is discussed. Flights over well known test areas are used to prove the accuracy of derived data products. Differences in data processing methods are pointed out in comparison to sensor systems based on CCD matrices or film.

Enhanced retinal image registration accuracy using expectation maximisation and variable bin-sized mutual information

While retinal images (RI) assist in the diagnosis of various eye conditions and diseases such as glaucoma and diabetic retinopathy, their innate features including low contrast homogeneous and non-uniformly illuminated regions, present a particular challenge for retinal image registration (RIR). Recently, the hybrid similarity measure, Expectation Maximization for Principal Component Analysis with Mutual Information (EMPCA-MI) has been proposed for RIR. This paper investigates incorporating various fixed and adaptive bin size selection strategies to estimate the probability distribution in the mutual information (MI) stage of EMPCA-MI, and analyses their corresponding effect upon RIR performance. Experimental results using a clinical mono-modal RI dataset confirms that adaptive bin size selection consistently provides both lower RIR errors and superior robustness compared to the empirically determined fixed bin sizes.

Fast EM Principal Component Analysis Image Registration Using Neighbourhood Pixel Connectivity

Image registration IR is the systematic process of aligning two images of the same or different modalities. The registration of mono and multimodal images i.e., magnetic resonance images, pose a particular challenge due to intensity non-uniformities INU and noise artefacts. Recent similarity measures including regional mutual information RMI and expectation maximisation for principal component analysis with MI EMPCA-MI have sought to address this problem. EMPCA-MI incorporates neighbourhood region information to iteratively compute principal components giving superior IR performance compared with RMI, though it is not always effective in the presence of high INU. This paper presents a modified EMPCA-MI mEMPCA-MI similarity measure which introduces a novel pre-processing step to exploit local spatial information using 4-and 8-pixel neighbourhood connectivity. Experimental results using diverse image datasets, conclusively demonstrate the improved IR robustness of mEMPCA-MI when adopting second-order neighbourhood representations. Furthermore, mEMPCA-MI with 4-pixel connectivity is notably more computationally efficient than EMPCA-MI.