This blog post is in continuation of a previous post i.e. “A Very Brief History of Optical High Resolution Satellite Imaging”. For laymen, high resolution satellite images are fascinating as mere pictures containing earth features. But for the remote sensing analysts or experts, challenges start to come up while trying to quantify earth features through image processing algorithms. To attain accurate quantitative results from high resolution satellite images is subject to positional accuracy. If the images are not observed from exactly the same point in space, then they can have different displacements, which could cause geo-registration errors. Geometric or ortho-rectification (especially in mountain areas) of the satellite images is vital to overcome the distortions related to the sensor (e.g. jitter, view angle effects), satellite (e.g. attitude deviations from nominal), and Earth (e.g. rotation, curvature, relief).
All high resolution optical Earth Observation (EO) satellites are equipped with global navigation satellite systems (such as GPS), star sensors, and gyroscopes. Although most high resolution imaging sensors provide high resolution digital elevation models (DEMs) along with satellite images for accurate ortho-rectification, but due to cost factor of high resolution DEMs, analysts often prefer to rely on publicly freely available DEM data with the integration of Rational Polynomial Coefficient (RPC) files. The sources of distortion can be grouped into two broad categories: the Observer or the acquisition system (platform, imaging sensor and measuring instruments, such as gyroscope, stellar sensors, etc.) and the Observed (atmosphere and Earth). Factors such as sensor view angle, sun elevation and topography have a significant effect on the geometric properties of high resolution image (see table 1).
Table 1: Description of sources of error for the two categories, the Observer and the Observed, with the different sub-categories
||Description of error sources
||Platform (spaceborne or airborne)
||Variation of the movement
|Variation in platform attitude (low to high frequencies)
||Variation in sensor mechanics (scan rate, scanning velocity, etc.)
|Panoramic effect with field of view
||Time-variations or drift
||Refraction and turbulence
||Curvature, rotation, topographic effect
||Geoid to ellipsoid
|Ellipsoid to map
For geo-referencing or ortho-rectification of satellite images, several commercial and non-commercial algorithms are available. ERDAS Imagine, ENVI-IDL, PCI Geomatics, IDRISI, ESRI ArcMap and ArcView, Global Mapper etc. are most common and well known commercial softwares while GRASS, QGIS (Quantum GIS), PostGIS, uDig, gvSIG, etc. are open source softwares. Restore 1.0 software can perform image band operations, mathematical image calculations, bundle adjustment with self-calibration, image transformations, image enhancement, filter operations and rectification of any digital image. AutoGR Toolkit can perform automatic matching (scale, rotation and even color invariant) and geo-referencing in few seconds.. For time-saving and fast output products, batch processes through supercomputing technology are being implemented.
This example is based on GeoEye-1 (0.5 m resolution) satellites images of Dolakha District, Nepal. Two adjacent images were captured on 2nd November, 2009, with 25.4° off-nadir view angle having 40% overlay area. When both images were ortho-rectified using RPC and 20m topographic DEM, a huge displacement with no data between the images was observed (see Figure 1) with irregular shapes of tree crowns (see Figure 2). This distorted area (irregular shape) occurred in small patches and its distribution was not systematic. For example, in some places where there were less steep slopes (up to 40°), distorted parts did not occur. While processing high resolution satellite images, similarly you may find out geometric distortions.
Figure 1: GeoEye-1 image before ortho-rectification (on the left) and after ortho-rectification (on the right) through RPC files and 20m DEM.
Figure 2: Irregular shaped tree crowns due to off-nadir view angle, before (on the left) and after ortho-rectification (on the right).
Conflicts of Interest: The findings reported stand as scientific study and observations of the author and do not necessarily reflect as the views of author’s organizations.
About this post: This is a guest post by Hammad Gilani. Learn more about this blog’s authors here.