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)

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

FOSS4G date assessment on the isprs optical stereo satellite data. A benchmark for DSM generation

The ISPRS Working Group 4 Commission I on "Geometric and Radiometric Modelling of Optical Spaceborne Sensors", provides a benchmark dataset with several stereo data sets from space borne stereo sensors. In this work, the Worldview-1 and Cartosat-1 datasets are used, in order to test the Free and Open Source Software for Geospatial (FOSS4G) Digital Automatic Terrain Extractor (DATE), developed at Geodesy and Geomatics Division, University of Rome "La Sapienza", able to generate Digital Surface Models starting from optical and SAR satellite images. The accuracy in terms of NMAD ranges from 1 to 3 m for Wordview-1, and from 4 to 6 m for Cartosat-1. The results obtained show a general better 3D reconstruction for Worldview-1 DSMs with respect to Cartosat-1, and a different completeness level for the three analysed tiles, characterized by different slopes and land cover

Exploiting Sentinel-1 amplitude data for glacier surface velocity field measurements. Feasibility demonstration on baltoro glacier

The leading idea of this work is to continuously retrieve glaciers surface velocity through SAR imagery, in particular using the amplitude data from the new ESA satellite sensor Sentinel-1 imagery. These imagery key aspects are the free access policy, the very short revisit time (down to 6 days with the launch of the Sentinel-1B satellite) and the high amplitude resolution (up to 5 m). In order to verify the reliability of the proposed approach, a first experiment has been performed using Sentinel-1 imagery acquired over the Karakoram mountain range (North Pakistan) and Baltoro and other three glaciers have been investigated. During this study, a stack of 11 images acquired in the period from October 2014 to September 2015 has been used in order to investigate the potentialities of the Sentinel-1 SAR sensor to retrieve the glacier surface velocity every month. The aim of this test was to measure the glacier surface velocity between each subsequent pair, in order to produce a time series of the surface velocity fields along the investigated period. The necessary co-registration procedure between the images has been performed and subsequently the glaciers areas have been sampled using a regular grid with a 250 × 250 meters posting. Finally the surface velocity field has been estimated, for each image pair, using a template matching procedure, and an outlier filtering procedure based on the signal to noise ratio values has been applied, in order to exclude from the analysis unreliable points. The achieved velocity values range from 10 to 25 meters/month and they are coherent to those obtained in previous studies carried out on the same glaciers and the results highlight that it is possible to have a continuous update of the glacier surface velocity field through free Sentinel-1 imagery, that could be very useful to investigate the seasonal effects on the glaciers fluid-dynamics

Upgrade of foss date plug-in: Implementation of a new radargrammetric DSM generation capability

Synthetic Aperture Radar (SAR) satellite systems may give important contribution in terms of Digital Surface Models (DSMs) generation considering their complete independence from logistic constraints on the ground and weather conditions. In recent years, the new availability of very high resolution SAR data (up to 20 cm Ground Sample Distance) gave a new impulse to radargrammetry and allowed new applications and developments. Besides, to date, among the software aimed to radargrammetric applications only few show as free and open source. It is in this context that it has been decided to widen DATE (Digital Automatic Terrain Extractor) plug-in capabilities and additionally include the possibility to use SAR imagery for DSM stereo reconstruction (i.e. radargrammetry), besides to the optical workflow already developed. DATE is a Free and Open Source Software (FOSS) developed at the Geodesy and Geomatics Division, University of Rome "La Sapienza", and conceived as an OSSIM (Open Source Software Image Map) plug-in. It has been developed starting from May 2014 in the framework of 2014 Google Summer of Code, having as early purpose a fully automatic DSMs generation from high resolution optical satellite imagery acquired by the most common sensors. Here, the results achieved through this new capability applied to two stacks (one ascending and one descending) of three TerraSAR-X images each, acquired over Trento (Northern Italy) testfield, are presented. Global accuracies achieved are around 6 metres. These first results are promising and further analysis are expected for a more complete assessment of DATE application to SAR imagery

Climate change promotes hybridisation between deeply divergent species

Rare hybridisations between deeply divergent animal species have been reported for decades in a wide range of taxa, but have often remained unexplained, mainly considered chance events and reported as anecdotal. Here, we combine field observations with long-term data concerning natural hybridisations, climate, land-use, and field-validated species distribution models for two deeply divergent and naturally sympatric toad species in Europe (Bufo bufo and Bufotes viridis species groups). We show that climate warming and seasonal extreme temperatures are conspiring to set the scene for these maladaptive hybridisations, by differentially affecting life-history traits of both species. Our results identify and provide evidence of an ultimate cause for such events, and reveal that the potential influence of climate change on interspecific hybridisations goes far beyond closely related species. Furthermore, climate projections suggest that the chances for these events will steadily increase in the near future

Attentive Dual Stream Siamese U-net for Flood Detection on Multi-temporal Sentinel-1 Data

Due to climate and land-use change, natural disasters such as flooding have been increasing in recent years. Timely and reliable flood detection and mapping can help emergency response and disaster management. In this work, we propose a flood detection network using bi-temporal SAR acquisitions. The proposed segmentation network has an encoder-decoder architecture with two Siamese encoders for pre and post-flood images. The network's feature maps are fused and enhanced using attention blocks to achieve more accurate detection of the flooded areas. Our proposed network is evaluated on publicly available Sen1Flood11 benchmark dataset. The network outperformed the existing state-of-the-art (uni-temporal) flood detection method by 6\% IOU. The experiments highlight that the combination of bi-temporal SAR data with an effective network architecture achieves more accurate flood detection than uni-temporal methods.Comment: Accepted in IGARSS202

A CNN regression model to estimate buildings height maps using Sentinel-1 SAR and Sentinel-2 MSI time series

Accurate estimation of building heights is essential for urban planning, infrastructure management, and environmental analysis. In this study, we propose a supervised Multimodal Building Height Regression Network (MBHR-Net) for estimating building heights at 10m spatial resolution using Sentinel-1 (S1) and Sentinel-2 (S2) satellite time series. S1 provides Synthetic Aperture Radar (SAR) data that offers valuable information on building structures, while S2 provides multispectral data that is sensitive to different land cover types, vegetation phenology, and building shadows. Our MBHR-Net aims to extract meaningful features from the S1 and S2 images to learn complex spatio-temporal relationships between image patterns and building heights. The model is trained and tested in 10 cities in the Netherlands. Root Mean Squared Error (RMSE), Intersection over Union (IOU), and R-squared (R2) score metrics are used to evaluate the performance of the model. The preliminary results (3.73m RMSE, 0.95 IoU, 0.61 R2) demonstrate the effectiveness of our deep learning model in accurately estimating building heights, showcasing its potential for urban planning, environmental impact analysis, and other related applications

Investigating Imbalances Between SAR and Optical Utilization for Multi-Modal Urban Mapping

Accurate urban maps provide essential information to support sustainable urban development. Recent urban mapping methods use multi-modal deep neural networks to fuse Synthetic Aperture Radar (SAR) and optical data. However, multi-modal networks may rely on just one modality due to the greedy nature of learning. In turn, the imbalanced utilization of modalities can negatively affect the generalization ability of a network. In this paper, we investigate the utilization of SAR and optical data for urban mapping. To that end, a dual-branch network architecture using intermediate fusion modules to share information between the uni-modal branches is utilized. A cut-off mechanism in the fusion modules enables the stopping of information flow between the branches, which is used to estimate the network's dependence on SAR and optical data. While our experiments on the SEN12 Global Urban Mapping dataset show that good performance can be achieved with conventional SAR-optical data fusion (F1 score = 0.682 $\pm$ 0.014), we also observed a clear under-utilization of optical data. Therefore, future work is required to investigate whether a more balanced utilization of SAR and optical data can lead to performance improvements.Comment: 4 pages, 3 figures, accepted for publication in the JURSE 2023 Proceeding

Free global DSM assessment on large scale areas exploiting the potentialities of the innovative google earth engine platform

The high-performance cloud-computing platform Google Earth Engine has been developed for global-scale analysis based on the Earth observation data. In particular, in this work, the geometric accuracy of the two most used nearly-global free DSMs (SRTM and ASTER) has been evaluated on the territories of four American States (Colorado, Michigan, Nevada, Utah) and one Italian Region (Trentino Alto-Adige, Northern Italy) exploiting the potentiality of this platform. These are large areas characterized by different terrain morphology, land covers and slopes. The assessment has been performed using two different reference DSMs: the USGS National Elevation Dataset (NED) and a LiDAR acquisition. The DSMs accuracy has been evaluated through computation of standard statistic parameters, both at global scale (considering the whole State/Region) and in function of the terrain morphology using several slope classes. The geometric accuracy in terms of Standard deviation and NMAD, for SRTM range from 2-3 meters in the first slope class to about 45 meters in the last one, whereas for ASTER, the values range from 5-6 to 30 meters. In general, the performed analysis shows a better accuracy for the SRTM in the flat areas whereas the ASTER GDEM is more reliable in the steep areas, where the slopes increase. These preliminary results highlight the GEE potentialities to perform DSM assessment on a global scale