2. Outline
1. Introduction
2. Remote Sensing System
3. Electro Magnetic Spectrum
4. Digital Image Processing
5. Radiometric corrections
6. Geometric corrections
7. Image enhancement
8. Image classification
3. Self Introduction
Name: Venugopalan Nair
Education:
M.Sc. (Applied Geology), Barkatullah University, Bhopal, India
M.Tech (Remote Sensing), Bharathidasan University, Trichy, India
M.Tech (Hydrology), IIT, Roorkee, India
Experience: 15 Years + in GIS
National Geophysical Research Institute
GB Pant Institute of Himalayan Environment and Development
Defense Terrain Research Lab
Central Ground Water Board
RMSI
SBL
19. Structure of a digital Image
BSQ (Band Sequential Format):
Each line of the data followed immediately by the next line in the same
spectral band.
BIP (Band Interleaved by Pixel Format):
The first pixel for all bands in sequential order, followed by the second
pixel for all bands, followed by the third pixel for all bands, etc., interleaved
up to the number of pixels.
BIL (Band Interleaved by Line Format):
The first line of the first band followed by the first line of the second
band, followed by the first line of the third band, interleaved up to the
number of bands. Subsequent lines for each band are interleaved in similar
fashion.
45. Supervised Classification
• Advantages
– Analyst has control over the selected classes
tailored to the purpose
– Has specific classes of known identity
– Does not have to match spectral categories on the
final map with informational categories of interest
– Can detect serious errors in classification if
training areas are misclassified
46. Supervised Classification
• Disadvantages
– Analyst imposes a classification (may not be
natural)
– Training data are usually tied to informational
categories and not spectral properties
• Remember diversity
– Training data selected may not be representative
– Selection of training data may be time consuming
and expensive
– May not be able to recognize special or unique
categories because they are not known or small
48. Unsupervised Classification
• Advantages
– Requires no prior knowledge of the region
– Human error is minimized
– Unique classes are recognized as distinct units
• Disadvantages
– Classes do not necessarily match informational
categories of interest
– Limited control of classes and identities
– Spectral properties of classes can change with time
