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THESIS PROPOSAL
Course Name: Project & Thesis
Course Code: CSE 4100
DEPARTMENT OF CSE
OUR TOPIC
“Comparative performance analysis of
various method for detecting vegetation
area using satellite image”
PRESENTATION OVERVIEW
 Different method
 Background Study
 Problem statement
 Methodology
 Challenges
 Outcome
DIFFERENT METHODS
 NDVI
 VCI
 LAI
 GYURI
DIFFERENT METHODS
 NDVI (Normalized Difference Vegetation Index)
 Quantifies vegetation by measuring the difference
between near-infrared and visible red light.
NDVI=
(𝑁𝐼𝑅 −𝑅𝑒𝑑)
(𝑁𝐼𝑅+𝑅𝑒𝑑)
 Always ranges from -1 to +1
 High NDVI value detect healthier vegetation and low
NDVI value detect les vegetation
DIFFERENT METHODS
 VCI (Vegetation Condition Index)
 Compares the current NDVI to the range of values
observed in the same period in previous year
 Expressed in % and gives an idea where the
observed value is situated between the extreme
values in the previous years
 High VCI values indicate good and low VCI values
indicate bad vegetation state condition.
DIFFERENT METHODS
 LAI (Leaf Are Index)
 A measure for total area of leaves per unit ground
area and directly related to the amount of light that
can be intercepted by plants
 An important variable used to predict photosynthetic
primary production, eva-transpiration and as a
reference tool for crop growth
DIFFERENT METHODS
 GYURI (General Yield Unified Reference Index)
 Was deduced using a fitted double-Gaussian curve
 A significant distortion from the model gave
information of the possible stress of the field
PROBLEM STATEMENT
To find the best method for detecting
vegetation area using satellite image
METHODOLOGY
 Build and collect dataset by which
we perform our experiment
 Test various algorithms using the same
data
 Compare results of various algorithm
CHALLENGES
o Accuracy
 Collecting proper dataset
 Find out proper metrics for performance
measurement
o Time
OUTCOME
Find out the method that is fastest and
relatively accurate for moderately
medium sized data
THANKS!
Any questions?

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THESIS PROPOSAL.pptx

  • 1. THESIS PROPOSAL Course Name: Project & Thesis Course Code: CSE 4100 DEPARTMENT OF CSE
  • 2. OUR TOPIC “Comparative performance analysis of various method for detecting vegetation area using satellite image”
  • 3. PRESENTATION OVERVIEW  Different method  Background Study  Problem statement  Methodology  Challenges  Outcome
  • 4. DIFFERENT METHODS  NDVI  VCI  LAI  GYURI
  • 5. DIFFERENT METHODS  NDVI (Normalized Difference Vegetation Index)  Quantifies vegetation by measuring the difference between near-infrared and visible red light. NDVI= (𝑁𝐼𝑅 −𝑅𝑒𝑑) (𝑁𝐼𝑅+𝑅𝑒𝑑)  Always ranges from -1 to +1  High NDVI value detect healthier vegetation and low NDVI value detect les vegetation
  • 6. DIFFERENT METHODS  VCI (Vegetation Condition Index)  Compares the current NDVI to the range of values observed in the same period in previous year  Expressed in % and gives an idea where the observed value is situated between the extreme values in the previous years  High VCI values indicate good and low VCI values indicate bad vegetation state condition.
  • 7. DIFFERENT METHODS  LAI (Leaf Are Index)  A measure for total area of leaves per unit ground area and directly related to the amount of light that can be intercepted by plants  An important variable used to predict photosynthetic primary production, eva-transpiration and as a reference tool for crop growth
  • 8. DIFFERENT METHODS  GYURI (General Yield Unified Reference Index)  Was deduced using a fitted double-Gaussian curve  A significant distortion from the model gave information of the possible stress of the field
  • 9. PROBLEM STATEMENT To find the best method for detecting vegetation area using satellite image
  • 10. METHODOLOGY  Build and collect dataset by which we perform our experiment  Test various algorithms using the same data  Compare results of various algorithm
  • 11. CHALLENGES o Accuracy  Collecting proper dataset  Find out proper metrics for performance measurement o Time
  • 12. OUTCOME Find out the method that is fastest and relatively accurate for moderately medium sized data