Open source tool for DSMs generation from high resolution optical satellite imagery. Development and testing of an OSSIM plug-in

The fully automatic generation of digital surface models (DSMs) is still an open research issue. From recent years, computer vision algorithms have been introduced in photogrammetry in order to exploit their capabilities and efficiency in three-dimensional modelling. In this article, a new tool for fully automatic DSMs generation from high resolution satellite optical imagery is presented. In particular, a new iterative approach in order to obtain the quasi-epipolar images from the original stereopairs has been defined and deployed. This approach is implemented in a new Free and Open Source Software (FOSS) named Digital Automatic Terrain Extractor (DATE) developed at the Geodesy and Geomatics Division, University of Rome ‘La Sapienza’, and conceived as an Open Source Software Image Map (OSSIM) plug-in. DATE key features include: the epipolarity achievement in the object space, thanks to the images ground projection (Ground quasi-Epipolar Imagery (GrEI)) and the coarse-to-fine pyramidal scheme adopted; the use of computer vision algorithms in order to improve the processing efficiency and make the DSMs generation process fully automatic; the free and open source aspect of the developed code. The implemented plug-in was validated through two optical datasets, GeoEye-1 and the newest Pléiades-high resolution (HR) imagery, on Trento (Northern Italy) test site. The DSMs, generated on the basis of the metadata rational polynomial coefficients only, without any ground control point, are compared to a reference lidar in areas with different land use/land cover and morphology. The results obtained thanks to the developed workflow are good in terms of statistical parameters (root mean square error around 5 m for GeoEye-1 and around 4 m for Pléiades-HR imagery) and comparable with the results obtained through different software by other authors on the same test site, whereas in terms of efficiency DATE outperforms most of the available commercial software. These first achievements indicate good potential for the developed plug-in, which in a very near future will be also upgraded for synthetic aperture radar and tri-stereo optical imagery processing

Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery

Remote sensing technologies have been commonly used to perform greenhouse detection and mapping. In this research, stereo pairs acquired by very high-resolution optical satellites GeoEye-1 (GE1) and WorldView-2 (WV2) have been utilized to carry out the land cover classification of an agricultural area through an object-based image analysis approach, paying special attention to greenhouses extraction. The main novelty of this work lies in the joint use of single-source stereo-photogrammetrically derived heights and multispectral information from both panchromatic and pan-sharpened orthoimages. The main features tested in this research can be grouped into different categories, such as basic spectral information, elevation data (normalized digital surface model; nDSM), band indexes and ratios, texture and shape geometry. Furthermore, spectral information was based on both single orthoimages and multiangle orthoimages. The overall accuracy attained by applying nearest neighbor and support vector machine classifiers to the four multispectral bands of GE1 were very similar to those computed from WV2, for either four or eight multispectral bands. Height data, in the form of nDSM, were the most important feature for greenhouse classification. The best overall accuracy values were close to 90%, and they were not improved by using multiangle orthoimages

GEOMETRIC PROCESSING OF VERY HIGH-RESOLUTION SATELLITE IMAGERY: QUALITY ASSESSMENT FOR 3D MAPPING NEEDS

In recent decades, the geospatial domain has benefitted from technological advances in sensors, methodologies, and processing tools to expand capabilities in mapping applications. Airborne techniques (LiDAR and aerial photogrammetry) generally provide most of the data used for this purpose. However, despite the relevant accuracy of these technologies and the high spatial resolution of airborne data, updates are not sufficiently regular due to significant flight costs and logistics. New possibilities to fill this information gap have emerged with the advent of Very High Resolution (VHR) optical satellite images in the early 2000s. In addition to the high temporal resolution of the cost-effective datasets and their sub-meter geometric resolutions, the synoptic coverage is an unprecedented opportunity for mapping remote areas, multi-temporal analyses, updating datasets and disaster management. For all these reasons, VHR satellite imagery is clearly a relevant study for National Mapping and Cadastral Agencies (NMCAs). This work, supported by EuroSDR, summarises a series of experimental analyses carried out over diverse landscapes to explore the potential of VHR imagery for large-scale mapping

Digital surface modelling and 3D information extraction from spaceborne very high resolution stereo pairs

This report discusses the potentials of VHR stereo imagery for automatic digital surface modelling (DSM) and 3D information extraction on large metropolitan cities. Stereo images acquired by GeoEye-1 on Dakar and Guatemala City and by WorldView-2 on Panama City, Constitucion (Chile), Kabul, Teheran, Kathmandu and San Salvador were processed following a rigorous photogrammetric approach. The work focusing on evaluating the quality of the DSMs in relation to the image and terrain characteristics and, among the possible DSM’s application, present a solution for buildings height estimation. The size of the datasets, the variety of case studies and the complexity of the scenarios allow to critically analyzing the potentials of VHR stereo imagery for 3D landscape modeling for natural hazards assessment.JRC.G.2-Global security and crisis managemen

DSM Generation from Single and Cross-Sensor Multi-View Satellite Images Using the New Agisoft Metashape: The Case Studies of Trento and Matera (Italy)

DSM generation from satellite imagery is a long-lasting issue and it has been addressed in several ways over the years; however, expert and users are continuously searching for simpler but accurate and reliable software solutions. One of the latest ones is provided by the commercial software Agisoft Metashape (since version 1.6), previously known as Photoscan, which joins other already available open-source and commercial software tools. The present work aims to quantify the potential of the new Agisoft Metashape satellite processing module, considering that to the best knowledge of the authors, only two papers have been published, but none considering cross-sensor imagery. Here we investigated two different case studies to evaluate the accuracy of the generated DSMs. The first dataset consists of a triplet of Pléiades images acquired over the area of Trento and the Adige valley (Northern Italy), which is characterized by a great variety in terms of geomorphology, land uses and land covers. The second consists of a triplet composed of a WorldView-3 stereo pair and a GeoEye-1 image, acquired over the city of Matera (Southern Italy), one of the oldest settlements in the world, with the worldwide famous area of Sassi and a very rugged morphology in the surroundings. First, we carried out the accuracy assessment using the RPCs supplied by the satellite companies as part of the image metadata. Then, we refined the RPCs with an original independent terrain technique able to supply a new set of RPCs, using a set of GCPs adequately distributed across the regions of interest. The DSMs were generated both in a stereo and multi-view (triplet) configuration. We assessed the accuracy and completeness of these DSMs through a comparison with proper references, i.e., DSMs obtained through LiDAR technology. The impact of the RPC refinement on the DSM accuracy is high, ranging from 20 to 40% in terms of LE90. After the RPC refinement, we achieved an average overall LE90 <5.0 m (Trento) and <4.0 m (Matera) for the stereo configuration, and <5.5 m (Trento) and <4.5 m (Matera) for the multi-view (triplet) configuration, with an increase of completeness in the range 5–15% with respect to stereo pairs. Finally, we analyzed the impact of land cover on the accuracy of the generated DSMs; results for three classes (urban, agricultural, forest and semi-natural areas) are also supplied

Enhancment of dense urban digital surface models from VHR optical satellite stereo data by pre-segmentation and object detection

The generation of digital surface models (DSM) of urban areas from very high resolution (VHR) stereo satellite imagery requires advanced methods. In the classical approach of DSM generation from stereo satellite imagery, interest points are extracted and correlated between the stereo mates using an area based matching followed by a least-squares sub-pixel refinement step. After a region growing the 3D point list is triangulated to the resulting DSM. In urban areas this approach fails due to the size of the correlation window, which smoothes out the usual steep edges of buildings. Also missing correlations as for partly – in one or both of the images – occluded areas will simply be interpolated in the triangulation step. So an urban DSM generated with the classical approach results in a very smooth DSM with missing steep walls, narrow streets and courtyards. To overcome these problems algorithms from computer vision are introduced and adopted to satellite imagery. These algorithms do not work using local optimisation like the area-based matching but try to optimize a (semi-)global cost function. Analysis shows that dynamic programming approaches based on epipolar images like dynamic line warping or semiglobal matching yield the best results according to accuracy and processing time. These algorithms can also detect occlusions – areas not visible in one or both of the stereo images. Beside these also the time and memory consuming step of handling and triangulating large point lists can be omitted due to the direct operation on epipolar images and direct generation of a so called disparity image fitting exactly on the first of the stereo images. This disparity image – representing already a sort of a dense DSM – contains the distances measured in pixels in the epipolar direction (or a no-data value for a detected occlusion) for each pixel in the image. Despite the global optimization of the cost function many outliers, mismatches and erroneously detected occlusions remain, especially if only one stereo pair is available. To enhance these dense DSM – the disparity image – a pre-segmentation approach is presented in this paper. Since the disparity image is fitting exactly on the first of the two stereo partners (beforehand transformed to epipolar geometry) a direct correlation between image pixels and derived heights (the disparities) exist. This feature of the disparity image is exploited to integrate additional knowledge from the image into the DSM. This is done by segmenting the stereo image, transferring the segmentation information to the DSM and performing a statistical analysis on each of the created DSM segments. Based on this analysis and spectral information a coarse object detection and classification can be performed and in turn the DSM can be enhanced. After the description of the proposed method some results are shown and discussed

DSM GENERATION FROM HIGH RESOLUTION SATELLITE IMAGERY: DEVELOPMENT AND IMPLEMENTATION OF A NEW MATCHING STRATEGY

Negli ultimi anni sono stati fatti molti progressi tecnologici soprattutto nell’ambito della fotogrammetria satellitare, con la messa in orbita della seconda generazione di satelliti (EROS A e B, QuickBird, Ikonos II, WorldView, GeoEye-1, Cartosat dedicato all’acquisizione di stereocoppie), è possibile ormai utilizzare le immagini digitali ad alta risoluzione con precisione a terra dell’ordine del metro (varia da 0,40 m di GeoEye-1 a 2,5 m di Cartosat). Il telerilevamento satellitare ha dei pregi fondamentali come, per esempio, la possibilità di eseguire acquisizioni a intervalli regolari, garantendo così un monitoraggio continuo della zona, o la possibilità di acquisire dati in territori in via di sviluppo dove è più complicato e costoso organizzare dei voli fotogrammetrici. Viste le grandi potenzialità delle immagini pancromatiche satellitari ultimamente sono nati vari progetti di ricerca sull’interpretazione ed estrazione dei dati metrici. Una fondamentale operazione di elaborazione delle immagini satellitari è l’ortorettificazione. Questo processo consente di eliminare le distorsioni indotte dalla posizione e assetto del satellite rispetto alla Terra e dalle caratteristiche ottico-geometriche del sensore. Esistono due metodologie differenti per orto-rettificare le immagini, le funzioni polinomiali razionali (RPF-Rational Polynomial Function) o il modello rigoroso. Oltre al prodotto bidimensionale ottenuto dalla singola immagine satellitare orto-rettificata è possibile utilizzare una stereo-coppia per estrarre un modello digitale del terreno (Digital Elevation Model). Il DEM è la rappresentazione dei valori continui di elevazione sopra una superficie topografica con un array regolare di valori di quota, riferiti allo stesso datum (definizione dell’ESRI). Attualmente la realizzazione e l’utilizzo dei DEM ha subito un incremento sempre maggiore, dovuto al potenziale uso in molti campi di applicazione dalle cartografia alla geotecnica. Inoltre i DEM sono un’importante banca dati da cui poter ottenere molti prodotti secondari come curve di livello, profili, volumi e modelli di pendenza, ecc… La sempre maggiore capacità di calcolo degli elaboratori unita alla grande richiesta interdisciplinare dei DEM ha incrementato enormemente la continua ricerca di nuovi e più complessi algoritmi. Per l'estrazione dei DEM dalle immagini satellitari sono necessarie varie elaborazioni fotogrammetriche, si possono distinguere principalmente due fasi: l'orientamento delle immagini satellitari e il processo di matching. I modelli di orientamento adottati si dividono in modelli rigorosi e modelli generici. I primi utilizzano un approccio fotogrammetrico basato sulle equazioni di collinearità mentre nei secondi le coordinate immagine sono legate a quelle terreno mediante rapporti di polinomi di cui sono noti i coefficienti (RPC). Il matching è il processo che permette il riconoscimento dei punti omologhi fra le due immagini, ovvero dei medesimi punti a terra ripresi sui due fotogrammi. In questa maniera si ottiene una nuvola di punti corrispondenti nelle due immagini e così, conoscendo la geometria di acquisizione, si è in grado di costruire il modello tridimensionale del terreno. Possiamo distinguere due classi principali di algoritmi per il matching l'Area Based Matching (ABM) e il Feature Based Matching (FBM). L'ABM si basa sulla diretta correlazione tra i valori della radianza fra l'intorno del punto fissato sull’immagine master e l'intorno di un punto mobile sull'immagine slave. In questo modo si ricercano i punti omologhi dove è massimo il valore della correlazione. Il FBM ricerca prima delle features (punti, angoli, bordi o anche poligoni) in entrambe le immagini e in un secondo momento analizza la corrispondenza fra esse. Il tema principale della ricerca è stato quello di sviluppare una metodologia completa atta a processare il Matching, è stato quindi implementato un nuovo algoritmo nel quale la fase del Matching è stata combinata con quella di orientamento. La strategia usata per unificare le due fasi è stata quella di utilizzare i coefficienti RPC per caratterizzare le deformazione delle immagini stimando una serie di trasformazioni affini e poi impiegare un Least Square Matching (una particolare tecnica di ABM) guidato da queste trasformazioni affini per la ricerca della correlazione. Finita la fase di sviluppo, sono stati effettuati molti test, alcuni su di una stereo-coppia della zona costiera di Augusta (Siracusa - Sicilia - Italia) acquisita con il satellite ad alta risoluzione WorldView-1 e altri su una stereo-coppia di Roma acquisita con il sensore GeoEye-1. In questi test è stata valutata la robustezza, la precisione e l'accuratezza del nuovo software. Innanzitutto nei risultati è stata valutata l'accuratezza raggiunta tramite il confronto tra i DSM estratti e quelli di riferimento e inseguito sono state comparate le accuratezze tra il nuovo software e il software commerciale PCI Geomatics OrthoEngine. Le accuratezze raggiunte sono paragonabili o in certi casi inferiori rispetto a quelle ottenute con il software commerciale. In conclusione il nuovo algoritmo implementato nel software ha dato i risultati sperati aprendo nuove possibilità per uno sviluppo futuro dei processi di matching, attualmente sono in corso ulteriori ricerche riguardo l'utilizzo di una programmazione dinamica e adattiva dell'algoritmo.Surface Models (DSMs) have large relevance in many engineering, land planning and environmental applications for a long time. At present, the data required for the DSMs generation can be acquired by several sensors/techniques, among which airborne LiDAR, aerial photogrammetry, optical and radar spaceborne sensors play the major role. In this respect, the availability of new high resolution optical spaceborne sensors offers new interesting potentialities for DSMs generation, among which low cost, speed of data acquisition and processing and relaxed logistic requirements, quite important for the areas where the organization of aerial flights can be difficult for several motivations. Thanks to the very high resolution and the good radiometric quality of the images, it seems possible to extract DSMs comparable to middle scale aerial products; anyway, it has to be underlined that the DSM accuracy level is strictly related both to the quality of the stereo image orientation and to the effectiveness of the matching strategy. Two different types of orientation models are usually adopted: the physical sensor models (also called rigorous models or geometric reconstruction) and the generalized sensor models. The first one is based on a standard photogrammetric approach, where the image and the ground coordinates are linked through the collinearity equations, so that the involved parameters have a physical meaning. On the contrary, the generalized models are usually based on the Rational Polynomial Functions (RPFs), which link image and terrain coordinates through the Rational Polynomial Coefficients (RPCs) and eventual additional transformation parameters [Tao and Hu, 2001, 2002; Fraser and Hanley, 2003; Hanley and Fraser, 2004; Crespi et Al., 2009]. As regards the matching, it is well known that many different approaches have been developed in recent years. In all methods, the main step is to define the matching entity, that is a primitive chosen in the master image to be looked for in the slave image(s); basically, we can distinguish two techniques, the Area Based Matching (ABM) and the Feature Based Matching (FBM). In ABM methods, a small image window represents the matching primitive and the main strategies to assess similarity are cross-correlation and Least Squares Matching (LSM). FBM methods use, as main class of matching, basic features that are typically the easily distinguishable primitives in the input images, like corners, edges, lines, etc. [Gruen A. W. 1985; Jacobsen, 2006; Nascetti, 2009; Tang L. et al.,2002]. In addition, new matching strategies where ABM is used together with dynamic programming techniques were proposed during last decade [Birchfield S. and Tomasi C., 1998, 1999]; recently the quite promising technique of semi-global matching was proposed and applied to aerial imagery [Hirschmüller, 2008; Hirschmüller and Scharstein, 2009]. In this paper we present and discuss some results obtained with a new proprietary matching strategy for DSMs generation, which is implemented into the SISAR software developed at the Area di Geodesia e Geomatica – Università di Roma "La Sapienza". In order to assess the accuracy of the new strategy, some tests were carried out, using a stereo pair of Augusta coastal zone (Sicily, South Italy) acquired from WorldView-1 and one of the first available GeoEye-1 stereo pair, which was acquired over Rome. The results show that an accuracy at the level of about 2 m is achievable in open areas with both WorldWiew-1 and GeoEye-1 stereo pairs, whereas higher errors are displayed in urban areas. For WorldWiew-1 the results are still acceptable, being the accuracy at the level of 3 meters, but for GeoEye-1 the DSM extracted over a very dense urban area are much worse, with an accuracy at the level of 8-10 meters. Nonetheless, the new matching strategy has been proven effective, performing always better if compared with the one implemented into a well known and largely used software as PCI-Geomatics. In order to evaluate the potentiality of the new matching strategy and the accuracy of the extracted DSMs, some tests were carried out. In details, two stereo pairs acquired byWorld- View-1 and GeoEye-1 satellites have been used to compare the DSMs generated with the new strategy to those derived using the well known commercial software PCI Geomatics v.10.2 (OrthoEngine)

Geometric potential of Pléiades 1A satellite imagery

In this paper, the geometrical characteristics of Pléiades 1A satellite imagery (both single and stereo) are analysed. At first the process of digital surface model (DSM) extraction from a Pléiades 1A stereo pair is described and analysed. After that geometric an accuracy of imagery, orthorectified using the extracted DSM and using the SRTM (Shuttle radar topographic mission) was analysed. The Pléiades 1A stereo pair was acquired on October 22, 2012 from the same orbital pass over an urban zone (Kiev, Ukraine). The study area is heterogeneous: there are both built-up and flat areas. The iImage orientation, DSM extraction and orthorectified images generation were performed using the PCI Geomatica 2013 software. The results showed that a strong, positive correlation between reference-derived elevations and DSM-derived elevations can be observed, and the orthorectified image accuracy, generated using that DSM, approximately equal to 1 m can be achieved using a bias compensation sensor model. Different sensor models were used for orthorectification using the SRTM. In this case, the geometric accuracy is а function of a chosen sensor model and a number of ground control points (GCP)

Assessment of DEM derived from very high-resolution stereo satellite imagery for geomorphometric analysis

ABSTRACTVery high-resolution satellite stereo images play an important role in cartographical and geomorphological applications, provided that all the processing steps follow strict procedures and the result of each step is carefully assessed. We outline a general process for assessing a reliable analysis of terrain morphometry starting from a GeoEye-1 stereo-pair acquired on an area with different morphological features. The key steps were critically analyzed to evaluate the uncertainty of the results. A number of maps of morphometric features were extracted from the digital elevation models in order to characterize a landslide; on the basis of the contour line and feature maps, we were able to accurately delimit the boundaries of the various landslide bodies

Geometric Accuracy Assessment of Deimos-2 Panchromatic Stereo Pairs: Sensor Orientation and Digital Surface Model Production

Accurate elevation data, which can be extracted from very high-resolution (VHR) satellite images, are vital for many engineering and land planning applications. In this way, the main goal of this work is to evaluate the capabilities of VHR Deimos-2 panchromatic stereo pairs to obtain digital surface models (DSM) over different land covers (bare soil, urban and agricultural greenhouse areas). As a step prior to extracting the DSM, different orientation models based on refined rational polynomial coefficients (RPC) and a variable number of very accurate ground control points (GCPs) were tested. The best sensor orientation model for Deimos-2 L1B satellite images was the RPC model refined by a first-order polynomial adjustment (RPC1) supported on 12 accurate and evenly spatially distributed GCPs. Regarding the Deimos-2 based DSM, its completeness and vertical accuracy were compared with those obtained from a WorldView-2 panchromatic stereo pair by using exactly the same methodology and semiglobal matching (SGM) algorithm. The Deimos-2 showed worse completeness values (about 6% worse) and vertical accuracy results (RMSEZ 42.4% worse) than those computed from WorldView-2 imagery over the three land covers tested, although only urban areas yielded statistically significant differences (p < 0.05)