Lecture 7 - Introduction to image processing

Question 1

What consists a digital image?

Answer 1

Discrete picture elements called pixels

Question 2

How do we interpret images?

Answer 2

Tone - relative brightness
Size - of objects in image
Shape - form, structure, outline of objects
Texture - arrangement and frequency of tonal variation
Shadow - may provide indication of relative height and topography

Question 3

What can RGB colours be displayed as?

Answer 3

True colour composite
False colour composite

Question 4

False Colour

Answer 4

NIR in red, red in green and green in blue wavelengths

Question 5

What is an image histogram?

Answer 5

An image histogram is a graphical representation of the number of pixels (frequency) in an image as a function of their Brightness values (DN).

Question 6

Contrast Stretching

Answer 6

Expanding the original input brightness values to make it the same as the total dynamic range or sensitivty of the screen

Question 7

Types of Contrast enhancement

Answer 7

Linear
Histogram Equalisiation
Gaussian Stretch

Question 8

Image Enhancement

Answer 8

Improving how an image is displayed on the screen by using the entire brightness range but not changing the DN values in the image.

Question 9

Linear Contrast Stretch

Answer 9

Pixel values are linearly scaled with the lowest value being the lowest displayed and the highest value being the highest displayed.
Results in an increased contrast in the image.

Question 10

Why do we need to pre-process data?

Answer 10

Remove deficiencies in data such as atmospheric perturbation, radiometric fidelity, geometric erros

Question 11

When do we pre-process?

Answer 11

Comparing images acquired at different dates or times

Question 12

Radiometric Calibration

Answer 12

Process of converting DN to radiance, reflectance, or brightness temp.
Needed in order to use the imagery in quantitative measures e.g veg health, surface temp or water quality and time-series data