Automated Geometric Correction of High-resolution Pushbroom Satellite Data

Abstract

In this article, we present the use of the Automatic Ground control points Extraction technique (AGE) for increasing the automation in the geometric correction of high-resolution satellite imagery. The method is based on an image-to-image matching between the satellite data and an already geocoded image (i.e., a digital orthophoto). By using an adaptive least squares matching algorithm which implements a very robust outlier rejection technique, AGE can automatically measure many hundreds of topographic features (TFs) on the images, whose cartographic coordinates are derived from the geocoded image and elevations are extracted from an associated digital elevation model (DEM). The AGE technique has been tested for different high-resolution data: (a) 0.62-meter QuickBird panchromatic data (basic imagery processing level), (b) 2.5-meter SPOT-5/HRG panchromatic supermode data (standard 1B processing level), and (c) 1-meter Ikonos panchromatic data (standard Geo product processing level) collected in the Northern of Italy, both in flat (Torino Caselle test site) and mountain areas (Lecco test site). Regardless the relative image resolution between the satellite and the aerial data (1-meter) and regardless the processing level of the original satellite data, a similar TFs density has been obtained for both the QuickBird and the SPOT-5/HRG data (4.4 GCPs/km 2 and 4.1 GCPs/km 2) respectively, with a geometric accuracy for the GCPs extracted of 0.90 m for QuickBird and 3.90 m for SPOT-5/HRG. For the Ikonos imagery, AGE extracted a more dense set of GCPs (8.7 GCPs/km 2) but with a lower accuracy (3.19 m). The TFs identified with AGE can be used as GCPs for the rational polynomial coefficients (RPCs) computation and, therefore, for implementing a full automatic orthoimage generation procedure. By using the commercial off-the-shelf software PCI Geomatica ® v.9.1, orthoimages have been generated for all datasets. The geometric accuracy was verified on a set of 30 manually measured independent check points (CPs) and assessed a precision of 4.99 m RMSE for QuickBird, 5.99 m RMSE for SPOT-5/HRG, and 8.65 m RMSE for Ikonos. The use of a non-conventional image orthorectification technique implementing a neural network GCPs regularization, tested for the SPOT-5/HRG data, showed the full potential of the AGE method, allowing to obtain a 3.83 m RMSE orthoprojection in a fully automated way.

Geometric Accuracy Assessment and Correction of Imagery from Chinese Earth Observation Satellites (HJ-1 A/B, CBERS-02C and ZY-3)

“…This paper extends the work in (Gianinetto & Scaioni, 2008) with the GCPs collected by the operational accuracy assessment methodology and applies to the four Chinese satellites. The computed RPC has the following equations:…”

“…A successful experiment using the RPC computed from GCPs was reported in the work of (Gianinetto & Scaioni, 2008), which yet could not ensure an even distribution of GCPs due to its choice of GCP collection technology (feature matching in spatial domain), and its application was made to two satellites (i.e., IKONOS, QB) that have high quality in geometry and radiometry. This paper extends the work in (Gianinetto & Scaioni, 2008) with the GCPs collected by the operational accuracy assessment methodology and applies to the four Chinese satellites.…”

“…Although the computed RPC model could not fully recover the rigorous sensor geometry (Di, et al, 2003) (Xiong & Zhang, 2009), it is good enough to approximate the sensor model when some incompleteness of camera calibration or a large FOV exists (Gianinetto & Scaioni, 2008).…”

Optimal Ground Control Points for Geometric Correction Using Genetic Algorithm with Global Accuracy

“…Then GCPs are extracted from the category "stretches of water" or "water bodies". Carrion et al [2002], Chirici et al [2004] and Gianinetto et al [2004Gianinetto et al [ , 2008 proposed a new approach to high resolution orthoimage generation, based on the use of automatic GCPs extraction by means of a multi-resolution least squares matching routine. The implementation of GEOREF software has been completed and its operational application to upgrade spatial databases of satellite images has become possible.…”

Automatic Co-registration of Satellite Time Series via Least Squares Adjustment

“…When a large dataset is processed [Chirici et al, 2004], image matching algorithms [Gruen, 2012;Lemmens, 1988] can be used for automatically detecting corresponding CPs. Traditionally, the basic co-registration process works on pairs of images [Gianinetto and Scaioni, 2008]. Consequently, if the data set is composed of many images, each one has to be independently co-registered to another (one-to-one or sequential approach) 'master' image ( Fig.…”