Remote Sensing - a tool for environmental observation

54 geometric corrections there are many potential

Info icon This preview shows pages 68–71. Sign up to view the full content.

View Full Document Right Arrow Icon
5.4 Geometric Corrections There are many potential sources of geometric distortions of remotely sensed images (Richards, 1986): 1. the rotation of the earth during image acquisition; 2. the finite scan rate of some sensors; 3. the wide field of view of some sensors; 4. the curvature of the earth; 5. sensor non-idealities; 6. variations in platform altitude, attitude and velocity; 7. panoramic effects related to imaging geometry; 8. An inclination of the satellite required to obtain a full earth coverage (see ch.2: orbits).
Image of page 68

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
68 Point 3 is especially important for satellites such as NOAA-AVHRR with a large swath width (2700 k). Point 6 is not very important for satellite systems because their movements are rather stationary. For aircrafts, however, these effects can be very large. Figure 5.7 shows the 6 potential variations of aircraft motion and figure 5.8 their effect upon an image. The most commonly applied method to correct for geometric distortion is by comparing the position of specific points in the image with the corresponding coordinates of these points on a map. These points are called Ground Control Points or GCPs and are most often bridges, road crossings or large buildings. Based on several of these ground control points, geometric transformation functions can be computed and used to fit the image on the map coordinates. Transformation functions look like: u = f (x,y) v = g (x,y) The (x,y) coordinates describe the position of the GCPs on the map, (u,v) describe the coordinates of the GCPs in the image in terms of rows and columns. f and g are the transform- ation functions. For satellite imagery a first order geometric transformation is best to correct satellite images, airborne images sometimes require higher order transformations. However, higher order transformations require more GCPs. The accuracy of the transformation function is mostly given in RMS-error (root mean square error) and refers to the distance between the input location of a GCP and the retransformed location for the same GCP. Figure 5.7 The six most important flight parameters of an aircraft (Buiten & Clevers 1994).
Image of page 69
69 Figure 5.8 Geometric distortions of airborne remote sensing images by aircraft flight movements. Resampling Having determined the geometric transformation function by using GCPs the next step is to compute DN values for the new defined image grid (x,y) based on the DN values in the old grid (rows, columns). The spacing of the grid is chosen according to the pixel size required e.g. from 30 by 30 m to 50 by 50 m. As the old pixels will never fit exactly on the newly defined pixels an interpolation of the new DN values is necessary. This process is called resampling and comprises three techniques (figure 5.9): 1. Nearest neighbour resampling: the new pixel value is the nearest neighbour in the old grid.
Image of page 70

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 71
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern