Roland Perko

Forest Assessment Using High Resolution SAR Data in X-Band

Novel radar satellite missions also include sensors operating in X-band at very high resolution. The presented study reports methodologies, algorithms and results on forest assessment utilizing such X-band satellite images, namely from TerraSAR-X and COSMO-SkyMed sensors. The proposed procedures cover advanced stereo-radargrammetric and interferometric data processing, as well as image segmentation and image classification. A core methodology is the multi-image matching concept for digital surface modeling based on geometrically constrained matching. Validation of generated surface models is made through comparison with LiDAR data, resulting in a standard deviation height error of less than 2 meters over forest. Image classification of forest regions is then based on X-band backscatter information, a canopy height model and interferometric coherence information yielding a classification accuracy above 90%. Such information is then directly used to extract forest border lines. High resolution X-band sensors deliver imagery that can be used for automatic forest assessment on a large scale.

Assessment of the Stereo-Radargrammetric Mapping Potential of TerraSAR-X Multibeam Spotlight Data

TerraSAR-X can acquire image data in various resolutions down to a range of about 1 m. Moreover, the sensor can operate at various imaging beams and thus acquire image data at different off-nadir viewing angles. These circumstances led to a stimulation of the traditional stereo-mapping approach, as TerraSAR-X image pairs became available in high resolution and in various geometric dispositions. With respect to 3-D surface mapping, TerraSAR-X stereo data processing, therefore, is a serious alternative to synthetic aperture radar interferometry, which can be addressed as the evolving mapping technique of the last decade. Within the TerraSAR-X science program of the German Aerospace Center (DLR), high-resolution multibeam data sets in Spotlight mode were acquired for several Austrian test sites. In general, three images were obtained from either ascending or descending orbits. In order to exploit the 3-D mapping accuracy of TerraSAR-X, stereo-radargrammetric mapping techniques were applied to the data sets, thereby utilizing stereo pairs as well as multi-image data sets in various dispositions. This paper focuses on one of the selected test sites and refers to the issues of 2-D and 3-D mapping-accuracy assessment as well as to surface model and vegetation-height-model generation. Validation of these products was widely restricted to visual analysis due to the lack of adequate high-quality reference products.

The Epipolarity Constraint in Stereo-Radargrammetric DEM Generation

For stereometric processing of optical image pairs, the concept of epipolar geometry is widely used. It helps to reduce the complexity of image matching, which can be seen to be the most crucial step within a workflow to generate digital elevation models. In this paper, it is shown that this concept is also applicable to the cocircular geometry of synthetic aperture radar (SAR) image pairs. First, it is proven that, for any feasible SAR acquisition, the deviation from true epipolar geometry is within subpixel range and therefore acceptably small. Based on this, we propose a method to create “epipolar” geometry for arbitrary stereo configurations of any SAR sensor through appropriate geometric image transformations. Consequently, the semiglobal matching (SGM) algorithm can be applied, which is restricted to epipolar geometry and is thus known to be highly efficient. This innovative approach, integrating both epipolar transformation and SGM, has been applied to a TerraSAR-X stereo data set. Its benefit has been demonstrated in a comparative assessment with respect to results, which have been previously achieved on the same test data using state-of-the-art stereometric methods.

Mapping Tropical Rainforest Canopy Disturbances in 3D by COSMO-SkyMed Spotlight InSAR-Stereo Data to Detect Areas of Forest Degradation

Assessment of forest degradation has been emphasized as an important issue for emission calculations, but remote sensing based detecting of forest degradation is still in an early phase of development. The use of optical imagery for degradation assessment in the tropics is limited due to frequent cloud cover. Recent studies based on radar data often focus on classification approaches of 2D backscatter. In this study, we describe a method to detect areas affected by forest degradation from digital surface models derived from COSMO-SkyMed X-band Spotlight InSAR-Stereo Data. Two test sites with recent logging activities were chosen in Cameroon and in the Republic of Congo. Using the full resolution COSMO-SkyMed digital surface model and a 90-m resolution Shuttle Radar Topography Mission model or a mean filtered digital surface model we calculate difference models to detect canopy disturbances. The extracted disturbance gaps are aggregated to potential degradation areas and then evaluated with respect to reference areas extracted from RapidEye and Quickbird optical imagery. Results show overall accuracies above 75% for assessing degradation areas with the presented methods.

Airborne Based High Performance Crowd Monitoring for Security Applications

Crowd monitoring in mass events is a highly important technology to support the security of attending persons. Proposed methods based on terrestrial or airborne image/video data often fail in achieving sufficiently accurate results to guarantee a robust service. We present a novel framework for estimating human density and motion from video data based on custom tailored object detection techniques, a regression based density estimate and a total variation based optical flow extraction. From the gathered features we present a detailed accuracy analysis versus ground truth information. In addition, all information is projected into world coordinates to enable a direct integration with existing geo-information systems. The resulting human counts demonstrate a mean error of 4% to 9% and thus represent a most efficient measure that can be robustly applied in security critical services.

Mobile Augmented Reality for Tourists - MARFT

The aim of the project MARFT is to demonstrate the next generation of augmented reality targeting current mass market mobile phones. MARFT sets out to launch an interactive service for tourists visiting mountainous rural regions. During local trips they will be able to explore the surrounding landscape by pointing the lens of the smart-phone camera towards the area of interest. As soon as the view-finder shows the area of interest, the tourist will be able to choose between two products: (i) an augmented photo superimposed with tourist information like hiking tours or lookout points or (ii) a rendered 3D virtual reality view showing the same view as the real photo also augmented with tourist objects. The outstanding step beyond current augmented reality applications is that MARFT is able to augment the reality with cartographic accuracy. In addition to the benefit of presenting reliable information, MARFT is able to consider the visibility of objects and further to work completely offline in order to avoid roaming costs especially for tourists visiting from abroad.

Assessment of boreal forest height from WorldView-2 satellite stereo images

ABSTRACT WorldView-2 (WV2) satellite stereo images were used to derive a digital surface model, which together with a high-resolution digital terrain model from airborne laser scanning (ALS) were used to estimate forest height. Lorey’s mean height (HL) could be estimated with a root mean square error of 1.5 m (8.3%) and 1.4 m (10.4%), using linear regression, at the two Swedish test sites Remningstorp (Lat. 58°30ʹN, Long. 13°40ʹE) and Krycklan (Lat. 64°16ʹN, Long. 19°46ʹE), which contain hemi-boreal and boreal forest. The correlation coefficients were r = 0.94 and r = 0.91, respectively. The 10 m sample plots were 175 in Remningstorp and 282 in Krycklan. It was furthermore found that WV2 data are sometimes unstable for canopy top height estimations (ALS height percentile 100, p100) and that the reconstructed heights are generally located below the actual top height. The WV2 p60 was found to correlate best with ALS p70 in Remningstorp, while WV2 p95 was found to correlate best with ALS p70 in Krycklan, and it moreover reached the highest correlation for all other estimated variables, at both test sites. It was concluded that WV2 p95 height data overall represent approximately the forest height ALS p70. The overall high correlation coefficients above 0.90 at both test sites, with different forest conditions, indicate that stereo matching of WV2 satellite images is suitable for forest height mapping.

Using worldwide available TerraSAR-X data to calibrate the geo-location accuracy of optical sensors

A method to calibrate the geo-location accuracy of optical sensors is presented which is based on a novel multi-modal image matching strategy. This concept enables to transfer points from highly accurate TerraSAR-X imagery to optical images. These points are then used to register the images or to update the optical sensor models. The potential of the methodology is demonstrated on Spot 5, Ikonos and RapidEye images.

Novelty-based Spatiotemporal Saliency Detection for Prediction of Gaze in Egocentric Video

The automated analysis of video captured from a first-person perspective has gained increased interest since the advent of marketed miniaturized wearable cameras. With this a person is taking visual measurements about the world in a sequence of fixations which contain relevant information about the most salient parts of the environment and the goals of the actor. We present a novel model for gaze prediction in egocentric video based on the spatiotemporal visual information captured from the wearers camera, specifically extended using a subjective function of surprise by means of motion memory, referring to the human aspect of visual attention. Spatiotemporal saliency detection is computed in a bioinspired framework using a superposition of superpixel- and contrast based conspicuity maps as well as an optical flow based motion saliency map. Motion is further processed into a motion novelty map that is constructed by a comparison between most recent motion information with an exponentially decreasing memory of motion information. The innovative motion novelty map is experienced to be able to provide a significant increase in the performance of gaze prediction. Experimental results are gained from egocentric videos using eye-tracking glasses in a natural shopping task and prove a 6.48% increase in the mean saliency at a fixation in terms of a measure of mimicking human attention.

DEM-based epipolar rectification for optimized radargrammetry

The quality of DEMs derived via radargrammetry mainly depends on the similarity of the SAR stereo images, taken under different look angles, in the image matching step. This work presents a novel pre-processing method that allows generating more similar epipolar images which are corrected by the underlying topography thus leading SAR specific distortion corrected stereo pairs. The evaluation w.r.t LiDAR data shows the increased quality of resulting DEMs.