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Im Bergbau ist die Interaktion mit dem Wasserhaushalt von besonderer Bedeutung. Über die Veränderung des Wasserhaushaltes kann beispielsweise auf Auswirkungen des Bergbaus auf die Umgebung, gewollt oder ungewollt, geschlossen werden. Diese Auswirkungen zu detektieren und daraus auf Landschaftsauswirkungen zu schließen, ist eine relevante Fragestellung. Da die zu beobachtenden Gebiete häufig eine große Ausdehnung haben und eine zyklische gebietsabdeckende manuelle Kontrolle häufig sehr kostenintensiv ist, eignet sich die Fernerkundung, die direkte Prozesse auf der Erdoberfläche beobachtet, dazu, ein Monitoring derartiger Gebiete hinsichtlich dieser Prozesse durchzuführen. Dabei können mehrere Faktoren beobachtet werden: Über Radarfernerkundung können Veränderungen der Bodenfeuchte, über spektrale Analysen Veränderungen der Vegetationsvitalitäten ermittelt werden. Über die Interpretationen multispektraler Daten können neue oder vergrößerte Wasserflächen detektiert werden. Zudem besteht die Möglichkeit, wassermanagementinduzierte Bodenbewegungen (beispielsweise Hebungen durch Grubenwasseranstieg) mit interferometrischen Radardaten zu detektieren und zu messen. Ein Monitoring mit diesen Verfahren ist für größere Flächen möglich. Als Fernerkundungssensoren eignen sich die Copernicus-Satelliten Sentinel-1 und 2, die zusätzlich den großen Vorteil haben, dass die Daten in kurzen Perioden kostenfrei und operationell verfügbar sind. Im vorliegenden Paper sind mehrere mögliche Monitoringkomponenten beschrieben, die im Rahmen verschiedener Forschungsvorhaben entstanden sind. Hydrological monitoring is important for mining activities. For example, a change of hydrological conditions might indicate an impact of mining activities in the neighborhood. The detection of such impacts is a relevant task. The observed areas are usually large, and a cyclic manual inspection is often very expensive. Therefore, remote sensing is suitable to do a monitoring of such areas in order to detect different processes. Changes in soil moisture can be detected by looking into backscattering characteristics of radar images, spec-trum analysis of optical images is able to assess vegetation vitality. The interpretation of multispectral data is able to show new and enlarged water bodies. Furthermore, ground deformation caused by hydrological changes (for example uplifts as a consequence of increasing pit water) can be detected and measured via radar interferometry. A monitoring with such methods is possible for large areas. As remote sensing sensors the Copernicus-satellites Sentinel-1 and 2 are suitable with the big advantage of providing data in short cycles operationally and free of charge. In this paper various monitoring components are described.

ESRI;Hexagon, 22nd Congress of the International Society for Photogrammetry and Remote Sensing: Imaging a Sustainable Future, ISPRS 2012 -- 25 August 2012 through 1 September 2012 -- -- 129546, In recent years, Synthetic Aperture Radar (SAR) data have been widely used for scientific applications and several SAR missions were realized. The active sensor principle and the signal wavelength in the order of centimeters provide all-day and all-weather capabilities, respectively. The modern German TerraSAR-X (TSX) satellite provides high spatial resolution down to one meter. Based on such data SAR Interferometry may yield high quality digital surface models (DSMs), which includes points located on 3d objects such as vegetation, forest, and elevated man-made structures. By removing these points, digital elevation model (DEM) representing the bare ground of Earth is obtained. The primary objective of this paper is the validation of DEMs obtained from TSX SAR data covering Barcelona area, Spain, in the framework of a scientific project conducted by ISPRS Working Group VII/2 "SAR Interferometry" that aims the evaluation of DEM derived from data of modern SAR satellite sensors. Towards this purpose, a DSM was generated with 10 m grid spacing using TSX StripMap mode SAR data and converted to a DEM by filtering. The accuracy results have been presented referring the comparison with a more accurate (10 cm-1 m) digital terrain model (DTM) derived from large scale photogrammetry. The results showed that the TSX DEM is quite coherent with the topography and the accuracy is in between ±8-10 m. As another application, the persistent scatterer interferometry (PSI) was conducted using TSX data and the outcomes were compared with a 3d city model available in Google Earth, which is known to be very precise because it is based on LIDAR data. The results showed that PSI outcomes are quite coherent with reference data and the RMSZ of differences is around 2.5 m.

Remote sensing (RS) image segmentation is an essential step in geographic object-based image analysis (GEOBIA) to ultimately derive “meaningful objects”. While many segmentation methods exist, most of them are not efficient for large data sets. Thus, the goal of this research is to develop an efficient parallel multi-scale segmentation method for RS imagery by combining graph theory and the fractal net evolution approach (FNEA). Specifically, a minimum spanning tree (MST) algorithm in graph theory is proposed to be combined with a minimum heterogeneity rule (MHR) algorithm that is used in FNEA. The MST algorithm is used for the initial segmentation while the MHR algorithm is used for object merging. An efficient implementation of the segmentation strategy is presented using data partition and the “reverse searching-forward processing” chain based on message passing interface (MPI) parallel technology. Segmentation results of the proposed method using images from multiple sensors (airborne, SPECIM AISA EAGLE II, WorldView-2, RADARSAT-2) and different selected landscapes (residential/industrial, residential/agriculture) covering four test sites indicated its efficiency in accuracy and speed. We conclude that the proposed method is applicable and efficient for the segmentation of a variety of RS imagery (airborne optical, satellite optical, SAR, high-spectral), while the accuracy is comparable with that of the FNEA method. (VLID)2600370

Geographic Object-Based Image Analysis (GEOBIA) techniques have become increasingly popular in remote sensing. GEOBIA has been claimed to represent a paradigm shift in remote sensing interpretation. Still, GEOBIAsimilar to other emerging paradigmslacks formal expressions and objective modelling structures and in particular semantic classification methods using ontologies. This study has put forward an object-based semantic classification method for high resolution satellite imagery using an ontology that aims to fully exploit the advantages of ontology to GEOBIA. A three-step workflow has been introduced: ontology modelling, initial classification based on a data-driven machine learning method, and semantic classification based on knowledge-driven semantic rules. The classification part is based on data-driven machine learning, segmentation, feature selection, sample collection and an initial classification. Then, image objects are re-classified based on the ontological model whereby the semantic relations are expressed in the formal languages OWL and SWRL. The results show that the method with ontologyas compared to the decision tree classification without using the ontologyyielded minor statistical improvements in terms of accuracy for this particular image. However, this framework enhances existing GEOBIA methodologies: ontologies express and organize the whole structure of GEOBIA and allow establishing relations, particularly spatially explicit relations between objects as well as multi-scale/hierarchical relations. (VLID)2195633

Son yıllarda, yapay açıklıklı radar (SAR) verileri bilimsel uygulamalarda yoğun bir şekilde kullanılmış ve birçok SAR görevi gerçekleştirilmiştir. SAR teknolojisi, aktif algılama yeteneği ve uzun dalga boyları sayesinde pasif algılamaya dayalı çalışan optik sistemlerin aksine hava şartları ve gece-gündüzden bağımsız olarak günün her saati veri sağlayabilmektedir. SAR uydu verilerinin geçmişte en büyük problemi çözünürlük olarak kaydedilmiştir. Ancak bu problem, SAR teknolojisi için bir devrim niteliğinde olan TerraSAR-X (TSX)’in yörüngeye oturtulması ile aşılmıştır. TSX, üç farklı görüntüleme modu sayesinde geniş alanlar kaplayabilen yüksek çözünürlükte (1m’ye kadar) veriler üretebilmektedir. Günümüzde, TSX verileri kullanılarak çok sayıda bilimsel araştırma yapılmaktadır. ISPRS SAR Interferometri çalışma grubu da TSX SAR verilerinden elde edilen dijital yükseklik modellerinin (DYM) kalite değerlendirmesi konulu bir proje gerçekleştirmektedir. Proje kapsamında, Türkiye, İspanya, Almanya ve İtalya’dan seçilmiş test alanları kullanılmaktadır. Bu araştırmada, bahsedilen proje kapsamında 3m azimut çözünürlüklü TSX StripMap (SM) mod uydu görüntüleri kullanılarak İspanya’nın Barselona kentinde çok sayıda yüksek çözünürlüklü DYM üretilmiş ve bu modeller içerisinden en uygunu seçilerek kalite değerlendirmesi gerçekleştirilmiştir. Çalışmada, Barselona test alanı, dik ve dağlık topoğrafyaya sahip geniş bir alan ve kentsel alan olmak üzere iki kısma ayrılmış ve kalite değerlendirmesi doğruluk ve görsellik bileşenleri göz önünde bulundurularak yapılmıştır. Uygulamada dijital hava fotoğraflarından elde edilmiş 10 cm yatay ve 10 cm – 1 m düşey doğruluğa sahip bir DYM referans olarak kullanılmıştır. Sonuçlar TSX DYM’sinin topoğrafya ile her yönüyle uyumlu olduğunu ve doğruluğun ±8-10m arasında olduğunu göstermektedir. Çalışmada diğer bir uygulama olarak, LIDAR verileri ile üretilmiş ve çok hassas olduğu bilinen, Google Earth’de mevcut 3 boyutlu Barselona şehir modeli ile PSI (Persistent Scatterer Interferometri) tekniği ile TSX’den elde edilen sonuçlar karşılaştırılmıştır. Sonuç, PSI ürünlerinin referans veri ile oldukça uyumlu olduğu ve kotların karesel ortalama hatasının 2,5m dolayında olduğunu göstermiştir., In recent years, Synthetic Aperture Radar (SAR) data is being used intensely for scientific applications and many SAR missions have been accomplished. On the contrary of optical systems operating based on passive sensing principle, SAR technology is capable of providing data independent of weather conditions and time of acquisition under favour of active sensing ability and long wavelength. The most important problem of SAR data was decided upon the resolutuion. However, this problem is overcome with orbitting of TerraSAR-X (TSX) which is considered to be a revolution. TSX can provide high resolution data (up to 1m) covering large areas thanks to three different imaging modes. Today, many scientific studies are performed employing TSX data. ISPRS SAR Interferometry Working Group is also conducting a project that performs quality assessment of digital elevation models generated from TSX data. Within the scope of this project,several test sites are being studied from Turkey, Spain, Germany and Italy. In this research, many high resolution DEMs are generated using 3m azimuth resolution TSX StripMap (SM) mode images for Barcelona (Spain) and the most suitable one is chosen and quality asessment is performed on it in the context of the aforementioned project. For the study, quality assessment is performed taking into consideration accuracy and visuality components in Barcelona test site that has been partitioned in two sub-areas as a large area with steep and mountainous topography and an urban area. In the application, a photogrammetric reference model that has 10 cm horizontal and 10 cm – 1 m vertical accuracy was used for the verification. The results show that TSX DEM is compatible with all aspects of the topography with the accuracy between ±8-10m. As another application in the study, Barcelona city model available in Google Earth, which is known to be generated by very accurate LIDAR data, and PSI results of TSX are compared. As a result, it is observed that PSI products and reference data are quite compatible and RMSZ of elevations are around 2,5m.

International audience; In contrast to conventional airborne multi-echo laser scanner systems, full-waveform (FW) lidar systems are able to record the entire emitted and backscattered signals of each laser pulse. Instead of clouds of individual 3D points, FW devices provide 1D profiles of the 3D scene, which allows gaining additional and more detailed observations of the illuminated surfaces. Indeed, lidar waveforms are signals consisting of a train of echoes where each of them corresponds to a scattering target of the Earth surface or a group of close objects leading to superimposed signals. Modelling these echoes with the appropriate parametric function is necessary to retrieve physical information about these objects and characterize their properties. Henceforth, the extracted parameters can be useful for subsequent object segmentation and/or classification. This paper presents a stochastic based model to reconstruct lidar waveforms in terms of a set of parametric functions. The model takes into account both a data term which measures the coherence between the proposed configurations and the waveforms, and a regularizing term which introduces physical knowledge on the reconstructed signal. We search for the best configuration of functions by performing a Reversible Jump Markov Chain Monte Carlo sampler coupled with a stochastic relaxation. Finally, the algorithm is validated on waveforms from several airborne lidar sensors, showing the suitability of the approach even when the traditional assumption of Gaussian decomposition of waveforms is invalid.

International audience; In contrast to conventional airborne multi-echo laser scanner systems, full-waveform (FW) lidar systems are able to record the entire emitted and backscattered signal of each laser pulse. Instead of clouds of individual 3D points, FW devices provide connected 1D profiles of the 3D scene, which contain more detailed and additional information about the structure of the illuminated surfaces. This paper is focused on the analysis of FW data in urban areas. The problem of modelling FW lidar signals is first tackled. The standard method assumes the waveform to be the superposition of signal contributions of each scattering object in such a laser beam, which are approximated by Gaussian distributions. This model is suitable in many cases, especially in vegetated terrain. However, since it is not tailored to urban waveforms, the generalized Gaussian model is selected instead here. Then, a pattern recognition method for urban area classification is proposed. A supervised method using Support Vector Machines is performed on the FW point cloud based on the parameters extracted from the post-processing step. Results show that it is possible to partition urban areas in building, vegetation, natural ground and artificial ground regions with high accuracy using only lidar waveforms.

In a recent contribution Ferretti and co-workers (Ferretti, A., Fumagalli, A., Novali, F., Prati, C., Rocca, F., Rucci, A., 2011. A new algorithm for processing interferometric data-stacks: SqueeSAR IEEE Transactions on Geoscience and Remote Sensing 49(9), pp. 3460�3470) have proposed the SqueeSAR method, a way to exploit temporally coherent distributed scatterers in coherent SAR data stacks. Elevation and deformation or subsidence estimates are obtained with accuracy similar as in the well known persistent scatterer interferometry (PSI). In this paper we propose an alternative approach and provide a first demonstration of the optimal estimation of distributed scatterers� phase histories in urban areas. Different to SqueeSAR, we derive phase histories for each distributed scatterer pixel rather than for groups of pixels. We use the Anderson�Darling statistical test to identify neighboring samples of the same distribution. Prior to covariance matrix estimation required for maximum likelihood estimation we apply a multi-resolution defringe technique. By using TerraSAR-X high resolution spotlight data, it is demonstrated that we are able to retrieve reliable phase histories and motion parameter estimates from distributed scatterers with signal-to-noise-ratio far below the common range.

Bu çalışmada, çok-bantlı stereo hava görüntülerinden elde edilen çizgisel nesnelerin eşlenmesi ve üç boyutlu geri-çatımı için yeni bir yaklaşım sunulmuştur. Hava görüntülerinde var olan çok-bant bilgisi, ön-işleme ve kenar bulma aşamalarında tam olarak kullanılmıştır. Doğrusal çizgilerin başarılı bir şekilde çıkarılabilmesi amacıyla temel bileşenler analizi yöntemi uygulanmıştır. Stereo görüntülerdeki ilk eşlemeler, toplamda yedi adet ilişkisel koşul içeren yeni bir çift-tabanlı stereo çizgi eşleme yöntemi ile gerçekleştirilmiştir. Stereo görüntülerdeki nihai bire-bir çizgi eşlemeleri, üç adet yeni ölçüt ve son-benzerlik değerlendirilmesi kısımlarını içeren hassas eşleme aşamasında belirlenmiştir. Çizgi eşlemeleri bulunduktan sonra bu çizgilerin üç boyutlu geri-çatımı, çift-tabanlı çizgisel ilişkilerin doğasında bulunan ve mükerrer eşleme bilgisini kullanan yenilikçi bir yaklaşım ile gerçekleştirilmiştir. Bu sayede, epipolar çizgisine neredeyse paralel olarak bulunan stereo eşlemelerin geri-çatımının da başarılı bir şekilde yapılabilmesi mümkün olabilmektedir.Önerilen yaklaşım çeşitli yerleşim şekilleri barındıran iki farklı test alanında değerlendirilmiş ve oldukça başarılı ve umut verici stereo çizgi eşleme ve geri-çatım sonuçlarına ulaşılmıştır. Bunun yanı sıra, önerilen yaklaşımın sonuçları ile çizgi eşlemede başarısı kabul edilmiş ve bilinen bir yöntemin sonuçlarının karşılaştırması da önerilen yaklaşımın üstünlüğünü ve potansiyelini kanıtlamaktadır. In this study, a new approach for the matching and reconstruction of line features from multispectral stereo aerial images is presented. The advantages of the existing multispectral information in aerial images are fully taken into account all over the steps of pre-processing and edge detection. To accurately describe the straight line segments, a principal component analysis technique is adapted. The initial correspondences between the stereo images are generated using a new pair-wise stereo matching approach which involves a total of seven relational constraints. The final line to line correspondences between the stereo images are established in a precise matching stage in which the final line matches are assigned by means of three novel measures and a final similarity voting scheme. Once the line matches are established, the stereo reconstruction of those matches is performed by an innovative reconstruction approach that manipulates the redundancy inherent in line pair-relations. By this way, the reconstruction of the stereo matches that are observed in a nearly-parallel geometry with the epipolar lines can also be performed accurately.The proposed approach is tested over two different urban test sites with various built-up characteristics, and as a result, very successful and promising stereo line matching and reconstruction performances are reached. Besides, the comparison of the results of the proposed approach with the results of one of the state-of-the-art stereo matching approaches proves the superiority and the potential of proposed approach. 217