satellite process

1. Processing of Satellite Image
using Digital Image
Processing
Presenters:
Sana Ullah (4849)

2. Contents
 Image Processing
 Purpose of Image Processing
 Satellite Image
 Digital Image
 Color Composition
 False Color
 True Color
 Image Rectification and Restoration
 Ground Control Points (GCP)
 Bit Error
 Resampling
 Striping
 Image Enhancement
 Contrast Enhancement
 Spatial Filtering
 Information Extraction
 Image Classification
 Unsupervised Classification
 Supervised Classification
 Accuracy and Error
 Conclusion

3. Image Processing
• Analyzing and manipulating images with a computer for
information extraction is known as image processing.
Image processing generally involves three steps:
o Import an image with an optical scanner or directly through digital photography.
o Manipulate or analyze the image in some way. This stage can include image
enhancement and data compression, or the image may be analyzed to find
patterns that aren't visible by the human eye. For example, meteorologists use
image processing to analyze satellite photographs.
o Output the result. The result might be the image altered in some way or it might
be a report based on analysis of the image.

4. Purpose of Image Processing
• The purpose of image processing is divided into 5
groups. They are:
o Visualization - Observe the objects that are not visible.
o Image sharpening and restoration - To create a better image.
o Image retrieval - Seek for the image of interest.
o Measurement of pattern – Measures various objects in an image.
o Image Recognition – Distinguish the objects in an image.

5. Satellite Image
• Satellite images are images captured by satellites at
regular intervals (usually hourly) and used by
meteorologists to forecast the weather. The three types
of satellite imagery
o Weather zone are infrared images
o visible images
o water vapor images.

6. Digital Image
• Digital imaging is the art of making digital images – photographs,
printed texts, or artwork - through the use of a digital camera or
image machine, or by scanning them as a document.
• Each image is compiled of a certain amount of pixels, which are
then mapped onto a grid and stored in a sequence by a computer.
• Every pixel in an image is given a total value to determine its hue or
color.

7. Color Composition
There are two types of color composition :
• False Color
• True Color
• False color refers to a group of color rendering methods used to
display images in color which were recorded in the visual or non-
visual parts of the electromagnetic spectrum.
• A false-color image is an image that depicts an object in colors that
differ from those a photograph (a "true-color" image) would show.

8. Color Composition
• An image is called a "true-color" image when it offers a natural
color rendition, or when it comes close to it.
• This means that the colors of an object in an image appear to a
human observer the same way as if this observer were to directly
view the object:
o A green tree appears green in the image, a red apple red, a blue sky blue, and
so on.
o When applied to black-and-white images, true-color means that the perceived
lightness of a subject is preserved in its depiction.

9. Image Rectification and Restoration
Ground Control Points (GCP)
• Features with known locations on a map (X,Y coordinates). These
are the “ground control points”
• The same features can be accurately located on the images as well
(column, row numbers).
• The features must be well distributed on the map and the image.
• Highway intersections are commonly used ground control points.
Bit Error
• Salt and pepper effect due to random error
• Use 3x3 or 5x5 moving window average to remove the noise

10. Image Rectification and Restoration
Resampling
• The purpose is to assign pixel values to the empty pixels in the
rectified matrix output.
• Superimpose the rectified output matrix to the distorted image.
• The digital number (DN) of a pixel in the output matrix is assigned
based on the DN of its surrounding pixels in the distorted image.
Striping
• Malfunction of a detector
• Use gray scale adjustment to correct the strips

11. Striping
Resampling
Bit
Error

12. Image Enhancement
Contrast Enhancement
• Contrast generally refers to the difference in luminance or grey level
values in an image and is an important characteristic.
• It can be defined as the ratio of the maximum intensity to the minimum
intensity over an image.
• Contrast ratio has a strong bearing on the resolving power and detects
ability of an image.

13. Image Enhancement
Spatial Filtering
• Techniques are based on direct manipulation of pixels in an image.
• Used for filtering basics, smoothing filters, sharpening filters,
unsharp masking and laplacian.

14. Information Extraction
Image Classification
• Categorize all pixels in image.
• Spectral data.
• Combinations of DNs.
Two approaches for image classification:
• Unsupervised classification.
• Supervised Classification.

15. Information Extraction
Unsupervised Classification
• Examine unknown pixels in image.
• Comparing data with reference data.
• Common form of cluster “K-means”.
• Applied on sub-areas of image.
• Determine spectral separable class.
• More preferable than supervised.

16. Information Extraction
Supervised Classification
• Examine known pixels in image.
• Comparing data with reference data.
• Information classified.
Basic Steps in supervised procedure
• Training stage.
• Feature selection.
• Appropriate algorithm.
• Post classification smoothening.
• Accuracy assessment.

17. Information Extraction

18. Information Extraction
Accuracy and Error
• The comparison of a classification with ground-truth data to evaluate
how well the classification represents the real world.
• Observations per class.
• May take fewer samples of low variability classes like water/forest.
Problems in classification
• Sampling.
• Reliable ground data.
• Distribution.
Image Classifier
Ground Data

19. Information Extraction

20. Conclusion
Digital image processing of satellite data can be primarily grouped into
three categories:
• Image Rectification and Restoration,
• Enhancement and Information extraction.
• Image rectification is the pre-processing of satellite data for
geometric and radiometric connections.
• Enhancement is applied to image data in order to effectively display
data for subsequent visual interpretation.
• Information extraction is based on digital classification and is used
for generating digital thematic map.

21. References
• Digital Image Processing by Rafael C. Gonzalez.
• www.wikipedia.org.
• www.earthobservatory.nasa.org.
• IRSA Remote Sensing Image Processing System 5.2,6.0,2003.
• “Remote Sensing study based on IRSA Remote sensing Processing
System” IEEE Xplore no 0-7803-8742-2/04 (c)2004 IEEE.