Estrazione di layer vettoriali per utilizzo cartografico da immagini satellitari ad alta risoluzione.

Le immagini ad alta risoluzione riprese dai sensori spaziali, grazie alla loro crescente disponibilità e al continuo miglioramento degli algoritmi di ortorettifica resi disponibili agli utenti finali, sembrano poter diventare in un prossimo futuro utili strumenti per la produzione e l’aggiornamento di cartografia a media-grande scala. Attualmente, le possibilità di restituzione dalle immagini satellitari sono fortemente influenzate dalla difficoltà di ottenere coppie stereoscopiche (almeno per i satelliti a più alta risoluzione) e questo, di fatto, ne limita ancora l’utilizzo alla sola produzione di cartografia 2D. Evidentemente, il prodotto cartografico che può essere ricavato per questa via è diverso rispetto alla cartografia numerica 3D ricavata con metodi aero-fotogrammetrici: la terza dimensione dell’oggetto non può essere calcolata e la dimensione del pixel dei sensori attualmente disponibili per usi civili consente la produzione di cartografia esclusivamente alle scale comprese tra 1:10.000 e 1:5.000. In questo lavoro si presenta un primo test di estrazione di layer vettoriali (edifici e strade) da immagini IKONOS pancromatiche sulla zona del lago di Lecco, da utilizzare successivamente per la produzione di cartografia alla scala 1:10.000. I layer vettoriali estratti dalle immagini IKONOS sono stati confrontati con la CTR 1:10.000 e con alcune ortofoto dell’area test, verificando l’ottenimento delle precisioni planimetriche previste per la produzione della cartografia alla scala 1:10.000

Automatic topographic features extraction from Pléiades HR and OrbView-5 simulated data.

In the next future new high-resolution (HR) multispectral, hyperspectral and radar satellite will be launched. In particular, from 2007 it is expected to be launched the first European satellite constellation with sub-meter resolution ORFEO (Optical and Radar Federated Earth Observation), composed of two French optical satellites (Pléiades HR) with 0.70 m resolution and four Italian X-band SAR satellites (COSMO-SkyMed). In the same year it is planned to be operative the first commercial ultra-high-resolution (UHR) satellite, OrbView-5, being able to collect imagery with 0.41 m resolution. This paper presents a first test of automatic topographic feature extraction – by means of the Automatic Ground control points Extraction (AGE) technique developed in the last years – for the future Pléiades HR and the OrbView-5 panchromatic data. Simulated images have been obtained both from 1:1,000 scale aerial othophotos and from a real QuickBird survey. Results showed that for the Pléiades HR satellites it is expected a metric precision of the extracted GCPs between 1.12 m and 2.07 m, while for the OrbView-5 it is expected a precision between 0.60 m and 1.10 m. These data are suitable for obtaining orthoimages with an RMSE better than 2.5 m for Pléiades HR and better than 1.5 m for OrbView-5, without any knowledge of the sensor model, of the satellite orientation and without any use of measured GCPs. Moreover, Plèiades HR results may be extended to the next generation Israeli EROS-B satellites, which will have a synchronous pushbroom high-resolution imaging camera with expected resolution of between 0.82 m and 0.70 m

A Sustainable Approach for upgrading geographic databases based on high resolution satellite imagery

The availability of high-resolution satellite images could be exploited for upgrading geographic databases at medium scales (1:5,000-1:25,000) as alternative to aerial photogrammetry. The paper presents a procedure to carry out this task which is based on an automatic image-to-image registration procedure of new satellite data to existing ortho-photomaps that have to be upgraded. In order to get a regularization of control points extracted in automatic way, a technique implementing a neural network algorithm is applied. Once an image has been georeferenced, this can be ortho-corrected thanks to a DTM (nowadays available in almost all developed countries). However, the product which is obtained so far is still a raster maps. To cope with the increasing need of vector data in geographic geographic databases, some tests performed on the extraction of features (buildings and roads) from real high-resolution satellite images have been performed and results are shown here. Finally, to complete the data acquisition process, the use of GPS-GIS data-logger receivers in differential mode is proposed

Hierarchical classification of complex landscape with VHR pan-sharpened satellite data and OBIA techniques

Land-cover/land-use thematic maps are a major need in urban and country planning. This paper demonstrates the capabilities of Object Based Image Analysis in multi-scale thematic classification of a complex sub-urban landscape with simultaneous presence of agricultural, residential and industrial areas using pan-sharpened very high resolution satellite imagery. The classification process was carried out step by step through the creation of different hierarchical segmentation levels and exploiting spectral, geometric and relational features. The framework returned a detailed land-cover/land-use map with a Cohen’s kappa coefficient of 0.84 and an overall accuracy of 85%

Satellite images geometric correction based on non-parametric algorithms and self-extracted GCPs

The geometric correction of high resolution satellite images can be carried out through generic non-parametric models that relates image to terrain coordinates. Traditional approaches to image geocoding relies on the measurement of a sufficient number of GCPs in both the ground and the image reference systems. Non-parametric models require a large number of GCPs well distributed on the whole scene, but the GCP identification and collection is a widely time-consuming operation and not always a simple task. Authors have developed two procedures for geometric correction based respectively on the Rational Function Model (RFM) and on a new neural network approach (MLP, Multi Layer Perceptron), and a procedure for automatic Ground Control Points (GCPs) extraction (AGE, Automatic GCPs Extraction) by means of a multi-resolution Least Squares Matching technique. This paper concerns about a new orthorectification procedure based on the sequential application of AGE, MLP and RFM algorithms for georeferencing high resolution satellite images. Tests have been carried out on Eros-A1 satellite images, using as reference maps available aerial orthoimages at a map scale of 1:10,000. A Case study is presented