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 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.

In this work, we introduce an end-to-end workflow for very high-resolution satellite-based mapping, building the basis for important 3D mapping products: (1) digital surface model, (2) digital terrain model, (3) normalized digital surface model and (4) ortho-rectified image mosaic. In particular, we describe all underlying principles for satellite-based 3D mapping and propose methods that extract these products from multi-view stereo satellite imagery. Our workflow is demonstrated for the Pléiades satellite constellation, however, the applied building blocks are more general and thus also applicable for different setups. Besides introducing the overall end-to-end workflow, we need also to tackle single building blocks: optimization of sensor models represented by rational polynomials, epipolar rectification, image matching, spatial point intersection, data fusion, digital terrain model derivation, ortho rectification and ortho mosaicing. For each of these steps, extensions to the state-of-the-art are proposed and discussed in detail. In addition, a novel approach for terrain model generation is introduced. The second aim of the study is a detailed assessment of the resulting output products. Thus, a variety of data sets showing different acquisition scenarios are gathered, allover comprising 24 Pléiades images. First, the accuracies of the 2D and 3D geo-location are analyzed. Second, surface and terrain models are evaluated, including a critical look on the underlying error metrics and discussing the differences of single stereo, tri-stereo and multi-view data sets. Overall, 3D accuracies in the range of $0.2$ to $0.3$ m in planimetry and $0.2$ to $0.4$ m in height are achieved w.r.t. ground control points. Retrieved surface models show normalized median absolute deviations around $0.9$ m in comparison to reference LiDAR data. Multi-view stereo outperforms single stereo in terms of accuracy and completeness of the resulting surface models.

Many scientific studies deal with person counting and density estimation from single images. Recently, convolutional neural networks (CNNs) have been applied for these tasks. Even though often better results are reported, it is often not clear where the improvements are resulting from, and if the proposed approaches would generalize. Thus, the main goal of this paper was to identify the critical aspects of these tasks and to show how these limit state-of-the-art approaches. Based on these findings, we show how to mitigate these limitations. To this end, we implemented a CNN-based baseline approach, which we extended to deal with identified problems. These include the discovery of bias in the reference data sets, ambiguity in ground truth generation, and mismatching of evaluation metrics w.r.t. the training loss function. The experimental results show that our modifications allow for significantly outperforming the baseline in terms of the accuracy of person counts and density estimation. In this way, we get a deeper understanding of CNN-based person density estimation beyond the network architecture. Furthermore, our insights would allow to advance the field of person density estimation in general by highlighting current limitations in the evaluation protocols.

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.