L3 Spectral Reflectance

Question 1

What is meant by spectral reflectance?

Answer 1

For a given substance/surface, the amount of solar radiation absorbed, transmitted or reflected varies with wavelength. The balance between all of these determines the surface’s colour

Question 2

What is a spectrometer?

Answer 2

This is a device that plots a graph showing the amount of radiation that a substance reflects in the different wavelengths

Question 3

What is the red edge?

Answer 3

This is the signature of vegetation on a spectrometer in which it does not reflect very much red visible light but does reflect a lot of infra-red radiation. The transition between them appears as a sharp edge on the spectrometer.

Question 4

Why does vegetation not reflect much red visible light?

Answer 4

Because it needs to absorb it for photosynthesis

Question 5

Why does vegetation reflect a lot of infra-red radiation?

Answer 5

Because photons in the longer infra-red wavelengths do not carry enough energy for photosynthesis

Question 6

What is the calculation for vegetation index and what is it a measure of?

Answer 6

Infra-red radiation reflection - red visible light reflection

Question 7

What is a slight problem for the vegetation index?

Answer 7

aspect affects the illumination of surfaces

Question 8

What is the Normalised Differences Vegetation Index (NDVI) a measure of?

Answer 8

the amount of photosynthetic activity present in a plant

Question 9

What is the calculation for NDVI?

Answer 9

(NIR - RED VL) / (NIR + RED VL)

Question 10

What is the Normalised Difference Snow Index (NDSI) used for?

Answer 10

Help distinguish clouds from ice in polar areas

Question 11

How do you calculate NDSI?

Answer 11

(SWIR - RED VL) / (SWIR + RED VL)

Question 12

What function do feature spaces provide towards spectral reflectance?

Answer 12

they are essentially the graphs in which the characteristics of different surfaces at different wavelengths are presented. You can combine these characteristics which are presented visually to identify what different surfaces are

Question 13

What is the common minimum number of bands that different satellites have and what does this provide towards understanding the spectral reflectance of different surfaces?

Answer 13

They usually have 7 or more which means we can detect how surfaces respond to 7 different wavelengths which is a lot of information we can use to detect different substances.

Question 14

What is the other way we can measure spectral reflectance other than % of reflectance?

Answer 14

Euclidean distance in a dimensional features space

Question 15

What happens when you use Euclidean distance method to identify different surfaces on a computer?

Answer 15

Image classificiation

Question 16

What are the two forms of image classification?

Answer 16

Supervised and unsupervised

Question 17

What are two benefits of classifying images?

Answer 17

it makes images easier to determine and interpret qualitatively and may reveal hidden information

Question 18

What is unsupervised classification?

Answer 18

Using spectral information to group pixels with similar spectral properties in to ‘clusters’. The computer does the work after you tell it how many clusters you want to identify

Question 19

What are 3 pros of using unsupervised classification?

Answer 19

No prior knowledge required
Low opportunity for human error
Unique classes recognised as distinct units

Question 20

What are 3 cons of using unsupervised classification?

Answer 20

spectrally homogenous classes may not correspond to informational categories of interest
limited control over classes
spectral properties of specific informational classes will change with time infringing upon ability for temporal comparison

Question 21

What is the process of unsupervised classification?

Answer 21

1) user specifies number of clusters
2) computer selects centroid positions for clusters based on euclidean distance for each pixel to centroid position
3) computer calculates new centroid position based on mean value of each cluster pixel then adjusts the euclidean distance calculation
4) process repeats until adjusting of pixels position to centroid position no longer changes

Question 22

What is supervised classification?

Answer 22

When images are classified based on information the user provides before the process begins

Question 23

How does the box classifier supervised classification work?

Answer 23

1) identify number of classes based on prior knowledge
2) create training sites for each class and define the spectral signature requirements for each
3) computer assigns pixels to each class
4) Each pixel is assigned to the different classes

Question 24

What happens if the pixel lies in the overlap between 2 or more boxes during box classifier supervised classification?

Answer 24

It uses minimum distance from the mean of each cluster to decide which class it should join

Question 25

What happens if the class box seems unrepresentative of te pixels distribution within it during box classifier supervised classification?

Answer 25

Adjust them using a parallel piped or box classifier

Question 26

What are 2 pros to the box classify supervised classification method?

Answer 26

Easy to program

fast in operation

Question 27

What are 4 cons of the box classify supervised classification method?

Answer 27

1) min and max values may not be truly representative
2) no other information used
3) the shape is not ideal for the nature of the data
4) potentially does not assign all pixels

Question 28

What is the process of minimum distance supervised classification?

Answer 28

1) finds cluster centroid positions
2) finds the closest cluster for a pixel based on its central position, even if the boundary for that cluster is much closer

Question 29

What are the pros of minimum distance supervised classification?

Answer 29

1) fast in operation

2) assigns all pixels

Question 30

What is the con of minimum distance supervised classification?

Answer 30

Ignores class variability

Question 31

What is the process of maximum likelihood supervised classification?

Answer 31

location, shape and size of clusters determined from statistical properties of training data.

Question 32

What is the maximum likelihood supervised classification regarded as?

Answer 32

Probably the best method due to its complexity and ensuing accuracy

Question 33

What is the maximum likelihood supervised classification method based upon?

Answer 33

Probability - how likely pixels are to be attributed to different cluster

Question 34

What is the pro of the maximum likelihood supervised classificaiton method?

Answer 34

usually the most accuratte

Question 35

What are 3 disadvantages of the maximum likelihood supervised classification method?

Answer 35

1) high computational cost
2) input data must be normally distributed
3) over classifies classes with large dispersion