15: Images & Image Processing Flashcards Preview

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Flashcards in 15: Images & Image Processing Deck (12)
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1
Q

WHAT IS IMAGE PROCESSING

A

collection of methods helping us improve images and understanding of what the image is

Example: make island boundary more distinct

2
Q

PRE AND POST PROCESSING STEPS

A
Preprocessing
aim: correct distortion / degraded image data to create a more faithful representation of original scene (on raw data)
types:
radiometric correction
geometric correction
Postprocessing
aim: improve image scene quality and help data interpretation, derive information from the data
types:
image enhancement
image classification
change detection
3
Q

LOW PASS FILTERS

A
  • “blurring”, “smoothing” or “moving average” filter - averages out rapid changes in intensity
  • simplest: calculates average of a pixel and all of its 8 immediate neighbours (3x3 kernel with averaging filter), then replaces original pixel value, process repeated for every image pixel
  • uses a kernel with an averaging filter, 3x3, 5x5, or 7x7, the larger the kernel the greater the averaging effect
4
Q

HIGH PASS FILTERS

A
  • does the opposite: sharpens appearance of fine detail in an image
  • first applies a low-pass filter to an image and then subtracts the result from the original, leaving behind only high spatial frequency information
  • directional, or edge detection filters: designed to highlight linear features e.g. roads or field boundaries, useful for vectorisation process
  • can also be designed to enhance features oriented in specific directions and are useful in applications, e.g. geology, for detection of linear features or structures
5
Q

NDVI

A

Normalised Difference Vegetation Index

Uses red and near infrared bands

NDVI = NIR - Red / NIR + Red

Normalised: to account for different time of day/year in which satellite imagery may have been acquired, therefore relative level of reflectivity is always changing, mitigate it by dividing it by the sum

  • shows plant ‘greenness’ / photosynthetic activity
  • commonly used vegetation indices
  • range -1 to +1
  • very low (<0.1): barren areas of rock, sand or snow
  • moderate. (0.2-0.3): shrub and grassland
  • high (0.6-0.8): temperate and tropical forest
6
Q

NDVI PROBLEMS

A

atmospheric effects:
actual composition of atmosphere, especially water vapour and aerosols, can affect measurements

clouds:
sensor can’t see through clouds + shadows/small clouds affect measurements

soil effects:
wet soils are darker, reflectance a direct function of water content

anisotropic effects:
all surfaces reflect differently in different directions

spectral effects:
each sensor has its own wavelength bands NDVI yields different values with different instruments

7
Q

Radiometric correction

A

correcting data for sensor irregularities and unwanted sensor / atmospheric noise; converting data so they accurately represent the reflected / emitted radiation measured by sensor

important because:
different images may need to be compared from different sensors / different times of day

8
Q

Geometric corrections

A

correcting for geometric distortions due to sensor-Earth geometry variations and conversion of data to real world coordinates (latitude and longitude) on Earth’s surface

important because:
earth’s surface is curved and some satellites don’t look straight down as they are restricted by orbit and will instead angle the sensor

9
Q

Spatial filtering

A

enhances or suppresses specific spatial patterns in an image

this technique explores distribution of pixels of varying brightness over an image and can detect and sharpen boundary discontinuities

types:
-low pass
-high pass
-edge enhancers and detectors
(all use histograms either for classification or filtering to determine clusters of pixel types)
10
Q

what is the result of the low pass filter?

A
  • reduces overall variability of an image
  • lowers contrast
  • reduces noise
  • blurs appearance of an image

why?
-emphasises large homogeneous areas of similar tone and reduce small detail in an image

  • smooth appearance of an image, remove random/periodic noise and reveal background pattern
  • average and median filters, often used for radar imagery
11
Q

high pass filter kernel

A

if there is no change in intensity, nothing happens. if one pixel is brighter than its immediate neighbours, it gets boosted

the sum of all ‘weights’ in the kernel is zero, the centre pixel is enhanced (has highest weight)

12
Q

Post processing: image classification

A
  • classifies pixels of raster image to certain categories
  • makes use of multispectral images, the more bands the more accurate the classification
  • unsupervised: computer based approach that assigns pixels to classes automatically, after the process you have to identify what the categories are (normally good if you can’t access the area/undertake ground-truthing)
  • supervised: user defines the training areas and tell the computer, then run classification which places areas into the classes and categories of interest