For Remote Sensing & GIS

1. Digital Image Processing
Lokhaze Ali

2. Image
• An image can be defined as a two-
dimensional signal (analog or digital),
that contains intensity (grayscale), or
color information arranged along an x
and y spatial axis.

3. Digital Image Processing
• Digital image processing is the use of
computer algorithms to perform image
processing on digital images. As a
subcategory or field of digital signal
processing, digital image processing has
many advantages over analog image
processing. It allows a much wider range of
algorithms to be applied to the input data
and can avoid problems such as the build-
up of noise and signal distortion during
processing.

4. Pixels
The discrete elements that make up a digital image are called Picture
Elements or Pixels

5. Sampling Rate
The sampling rate must be high enough to capture the required detail

6. Range of colors of Gray

7. Gaussian Blur Filter Effects
Embosing Filter

8. Brightness Transform Result
Grayscale Conversion Result

9. Brightness Transform Results
Contrast Transform Results

10. Image Resizing
To shrink an image to half its original size, we must discard half of
the image’s pixel information. To accomplish, throw out every other
row and column in the image.

11. Image Noise Examples

12. Resizing Algotithm
Color Interpolation in Stretching Image

13. Medical Image Processing

14. Medical Image Processing

15. Ultrasound

16. Weather Forecast - Tsunami

17. Atmospheric Study - Ozone
This shows the thickness of the Earth's ozone layer on January 27th
from 1982 to 2012. This atmospheric layer protects Earth from
dangerous levels of solar ultraviolet radiation. The thickness is measured
in Dobson units, in this image, smaller amounts of overhead ozone are
shown in blue, while larger amounts are shown in orange and yellow.

18. Motion Detector Cameras

19. Number of Rotations

20. Image Compressions

21. GradientShop: A novel approach
to image and video processing
• http://www.youtube.com/watch?v=MDa6NB
HAMfg

22. Tomorrow’s Vision
• http://www.youtube.com/watch?v=SPlxnYMZ
Kww&feature=related

23. Karst
• Karst is a characteristic geological feature of areas
comprised of limestone. Due to the solubility of these
rocks in water, exhibit an extreme heterogeneity of
hydraulic conductivities. The characterizing features
of karst aquifers are the open conduits, which provide low
resistance pathways for ground water flow. Overall cave
orientation is largely controlled by hydraulic gradient,
joint patterns and other tectonic features, such as
faulting and folding. The karst depressions may form on
the surface by subsurface actions (dissolution and
collapse). Thus, the depressions often show regularity of
pattern or alignments, frequently in association with
structurally guided cave systems below.

24. Karst Geomorphological Analysis

25. Applications in Remote Sensing

26. .
Applications in Remote Sensing
Quickbird band ratios for spectral enhancement of the land surface classification.
(a) A region of interest with several meso- and micro-sized karst depressions
(brownish colors; white arrows). (b) The iron oxide index (bands 3 by 1) that
enhances the separability of end members in classifications. Locations rich in
corresponding minerals are explicitly highlighted by high grey values (middle; white
colors). (c) The infrared-red index (bands 4 by 3), which allows improved
differentiation between vegetation types (bright colors) and non-vegetated areas
(dark tones). It is possible to detect karst depressions covered by plant species
like grassland (white ellipses). In order to conduct a precise karst landform
mapping, the latter must also be integrated into the desired indirect variable for
depressions along with bare sediment

27. Applications in Remote Sensing
• Subsets of DEM derivatives
(SRTM, left column; ASTER,
right column). (a and b)
Slope. (c and d) Aspect. (e
and f) Curvature. A large
pole, which can be detected
and compared on every
image, is marked by white
circles. Larger morphologic
units, such as valleys, are
generally displayed quite well
(e.g. a, upper left), whereas
micro- and meso-relief
landforms fall below the
resolving power of the data.

28. Applications in Remote Sensing
• Comparison of different remote sensing
data with regard to spatial resolution. All
satellite image subsets are displayed as
false color composites (a, b and d). The
Landsat ETM+ imagery (a; 30 m
resolution) fails to highlight small karst
depressions. Only assumptions about
existing landforms can be made. In
comparison, ASTER satellite data (b) are
more suitable due to their twofold higher
pixel size (15 m) but still perform poorly
as it is unclear if associated pixels
represent sinkholes or just sparse
vegetation clusters (red colors). The
scanned black and white aerial photograph
(c; spatial resolution about 0.5 m;
contrast enhanced) provides detailed
information about karst depressions filled
with sediment (darker tones). After data
fusion to 0.61 m ground sample distance
(gsd), Quickbird imagery (d) ideally
qualifies for karst feature

29. Hydrological Surface Analysis

30. Karst Geomorphological Map