Remote Sensing - a tool for environmental observation

Assigned to the display levels extreme black to

Info icon This preview shows pages 72–76. Sign up to view the full content.

View Full Document Right Arrow Icon
assigned to the display levels (extreme black to extreme white) on the basis of their frequency of occurrence i.e. the number of pixels with a specific grey-tone is equal for all grey-tone classes and consequently, all grey levels occur in equal portions on the screen. Density slicing Density slicing converts the continuous grey tone of an image into a series of density intervals (or slices) each corresponding to a specified digital range and assigns a colour to each range. This technique emphasizes subtle grey-tone differences by assigning colours. 5.6 Digital Image Filtering Spectral enhancement of images such as the previous described stretch techniques operates on each pixel of the image individually. Spatial enhancement techniques or convolution operations modifies pixel values based on the values of surrounding pixels. Spatial enhancements of most interest in remote sensing generally relate to smoothing of the image, edge detection and enhancement, and line detection. Such a digital filter operates by moving a template, window or kernel (often 3 by 3 pixels) over the image row-by-row and column-by-column. At each position of the template on top of the image a mathematical operation (sum, multiply, variance) is performed on the pixel values covered by the template. The response of this mathematical operation yields the new pixel value for the centre pixel of the template. Although templates of 3 by 3 pixels are most often used, templates of any size can be used. Examples of digital filters are the low pass filter aiming at image smoothing, the high pass filter aiming at image sharpening, the gradient filter aiming at enhancing edges or transition zones in the image, the Laplace
Image of page 72

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

View Full Document Right Arrow Icon
72 filter and the Sobel filter both also aiming at enhancing edges and suppressing noise. More complex filters assess e.g. the variability of spectral reflectance in the template (texture filter, variance filters or fractal filters). A thorough description of image digital image filtering and other digital image processing techniques is given by Gonzalez and Wintz (1987). Table 5.1 shows the templates of a number of digital filters, figure 5.11 illustrates the effect of some digital filters on a simple digital profile of an image. Figure 5.10 Principle of contrast stretch enhancement (Lillesand & Kiefer, 1994).
Image of page 73
73 Figure 5.11 The effect of a number of digital filter on the profile of a digital image.
Image of page 74

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

View Full Document Right Arrow Icon
74 ------------------------------------------------------------ 1 1 1 Low pass filter: 1/9 * 1 1 1 1 1 1 -1 -1 -1 High pass filter: -1 8 -1 -1 -1 -1 0 0 0 Gradient filter: -1 0 -1 0 0 0 0 -1 0 Laplace filter: -1 4 -1 0 -1 0 0 -1 0 Laplace + original: -1 5 -1 0 -1 0 v1 v4 v7 Median filter: v2 v5 v8 v3 v6 v9 1 1 1 Directional filter: 1/f * 0 0 0 1 1 1 ------------------------------------------------------------ Table 5.1 Some examples of digital image filters.
Image of page 75
Image of page 76
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