Geoscience Reference
In-Depth Information
use of a great variety of image processing methods.
h is chapter is concerned with the analysis and display of image data. h e
various ways that raster data can be stored on the computer are i rst explained
(Section 8.2). h e main tools for importing, manipulating and exporting
image data are then presented in Section 8.3. h is knowledge is then used
to process and to georeference satellite images (Sections 8.4 to 8.6). On-
screen digitization techniques are discussed in Section 8.7. Sections 8.8 and
8.9 deal with importing, enhancing, and analyzing images from laminated
lake sediments, including color-intensity measurements on transects across
the laminae. Finally, Sections 8.10 to 8.12 deal with automated grain size
analysis, charcoal quantii cation in microscope images, and the detection
of objects in microscope images on the basis of their shapes. h e Image
Processing Toolbox is used for the specii c examples throughout this chapter
(MathWorks 2014). While the MATLAB User's Guide to the Image Processing
Toolbox provides an excellent general introduction to the analysis of images,
this chapter provides an overview of typical applications in earth sciences.
8.2 Data Storage
Vector and raster graphics are the two fundamental methods for storing
pictures. h e typical format for storing
vector data
has already been introduced
in the previous chapter. In the following example the two columns in the i le
coastline.txt
represent the longitudes and latitudes of the points of a polygon.
NaN NaN
42.892067 0.000000
42.893692 0.001760
NaN NaN
42.891052 0.001467
42.898093 0.007921
42.904546 0.013201
42.907480 0.016721
42.910414 0.020828
42.913054 0.024642
(cont'd)
h e
NaN
s help to identify break points in the data (Section 7.2).
h e
raster data
are stored as 2D arrays. h e elements of these arrays
represent variables such as the altitude of a grid point above sea level, the
annual rainfall or, in the case of an image, the color intensity values.
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