1. 4.3 Digital Image Processing
Common image processing image analysis functions:
A. Preprocessing
B. Image Enhancement
C. Image Transformation
D. Image Classification and Analysis

2. C. Image Transformations
Manipulation of multiple bands of data
Generates a ‘new’ image
1. 3 band combinations
2. Spectral ratioing (arithmetic operations)
Vegetation indices
NDVI

3. 1. 3 band combinations
Significant advantage of multi-spectral imagery is ability to detect important differences between surface materials by combining spectral bands.
Band combinations are created by combining bands of spectral data to enhance the particular land cover of interest.

4. Landsat Thematic Mapper Imagery
Band Wavelength
0.45 to Blue Useful for distinguishing soil from vegetation.
0.52 to Green Useful for determining plant vigor.
to Red Matches chlorophyll absorption-used for discriminating vegetation types.
to Near IR Useful for determining biomass content.
1.55 to Short Wave IR Indicates moisture content of soil and veg.
to Thermal IR. Geological mapping, soil moisture, Thermal pollution monitoring and ocean current studies.
to Short Wave IR Ratios of 5 & 7 are used to map mineral deposits.

5. Near Infra Red Composite
Blue visible band is not used and the bands are shifted;
Visible green sensor band to the blue color gun
Visible red sensor band to the green color gun
NIR band to the red color gun.
Results in the familiar NIR composite with vegetation portrayed in red.

6. Bands 4, 3, 2

7. Near Infrared Composite (4,3,2)
Vegetation in NIR band is highly reflective
Shows veg in various shades of red
Water appears dark due to absorption

8. Popular band combination for vegetation studies, monitoring drainage and soil patterns and various stages of crop growth.
Vegetation - shades of red
Conifers darker red than hardwoods
lighter reds = grasslands or sparsely vegetated
Urban - cyan blue, light blue
Soils - dark to light browns.
Ice, snow and clouds - white or light cyan.

9. Bands 3,2,1

10. True Color composite
Visible bands are selected and assigned to their corresponding color guns to obtain an image that approximates true color.
Tends to appear flat and have low contrast due to scattering of the EM radiation in the blue visible region.

11. 3, 2, 1 Ground features appear in colors similar to their appearance
healthy veg = green
cleared fields = light
unhealthy veg = brown & yellow
roads = gray
shorelines = white
Water penetration - sediment and bathymetric info
Used for urban studies.
Cleared and sparsely vegetated areas are not as easily detected
Clouds and snow appear white and are difficult to distinguish.

12. Bands 7,4,2
In a SWIR composite, sensor band 7 is selected from the short-wave
infrared region.

13. Shortwave Infrared Composite (7,4,3 or 7,4,2)
SWIR composite image contains at least one shortwave infrared (SWIR) band.
Reflectance in SWIR region due primarily to moisture
SWIR bands are especially suited for camouflage detection, change detection, disturbed soils, soil type, and vegetation stress.

14. Provides striking imagery for desert regions
Provides a "natural-like" view, penetrates atmospheric particles and smoke.
Healthy veg = bright green
Barren soil = Pink
Sparse veg = oranges and browns
Dry veg = orange
Water = blue
Sands, soils and minerals - multitude of colors.
Fires = red - used in fire management
Urban areas = magenta
Grasslands - light green.
Conifers being darker green than deciduous
Provides striking imagery for desert regions
Useful for geological, agricultural and wetland studies

15. Use the spectral profile tool (Raster  Profile Tool) to examine the different spectral properties of a. water, b. vegetation and c. urban areas. Choose several pixels from each of the 3 categories and plot them.
Water
Blue Green Red Near IR

16. Agriculture
Blue Green Red Near IR

17. Urban
Blue Green Red Near IR

18. 2. Spectral ratioing - Using vegetation indices such as NDVI to study vegetation

19. Chlorophyll - Amount of chlorophyll in leaves affects the spectral signature in the visible.
Cells known as ‘spongy mesophyll’ are responsible for reflection of NIR.
Reflection occurs where the walls of these cells meet with air spaces inside the leaf.

20. Amount of near infrared energy reflected is a function of
Chlorophyll in healthy vegetation absorbs most of visible red and blue for photosynthesis.
Amount of near infrared energy reflected is a function of
internal structure
amount of moisture

21. Vegetation in imagery Distinct appearance in certain spectral bands
Multispectral imagery valuable for study of vegetation.
Distinct appearance in certain spectral bands
Distinguishes it from other objects in landscape.
Spectral signature varies with species and envir. factors
ID plants in various stages of life cycle or states of health.
Large areas can be studied quickly.
Esp. useful in remote areas (tropical rainforest)
Possible to obtain accurate quantitative information from imagery, together with field data.

22. Vegetation in imagery
Examples;
Est. # of acres of forest harvested for timber.
Predict regional or global yields of crops (wheat, soybeans)
Est. quantity of phytoplankton in oceans.

23. Healthy vegetation - high reflectance in NIR & low reflectance in red.

24. Landsat Thematic Mapper Imagery
Band Wavelength
to Blue
to Green
to Red
to Near IR
to Short Wave IR
to Thermal IR.
to Short Wave IR

27. Sometimes air spaces can be filled with water, thus a plant's state of hydration can significantly affect the reflectance in NIR.
Different species have different leaf cell structures, which affects reflectance of NIR.
Related factors – leaf size and orientation also affect reflectance of NIR.
For example, broad, thin leaves of deciduous plants are more reflective than needles of coniferous trees.

28. Most NIR that is not reflected by leaves is transmitted.
provides info to analyst
In a dense forest canopy, leaves underneath often reflect the energy transmitted by the top layer of leaves.
So, sections of a forest with a dense canopy will exhibit higher DN values in the near infrared band than sections with sparse canopy.

29. Differences among plant species;
amounts of chlorophyll
different leaf structures, shapes or orientation
causes species to absorb, reflect, or transmit differently.
Veg. may have different spectral signature when it is;
Emergent
Mature
Undergoing normal seasonal changes
Dormant
Healthy veg. contains more chl. than stressed or diseased.
Variations in spectral sigs. can be used to study vegetation through image interpretation.

30. When leaves lose their chlorophyll in autumn their spectral characteristics change.
Deciduous more reflective in NIR than conifers.

31. false-color composite - brightest red near river, indicating most vigorous vegetation, may be deciduous trees, shrubs, and grass.
darker red regions surrounding are coniferous forest.

32. Vegetative index - calculated (or derived) from remotely-sensed data to quantify vegetative cover on Earth's surface.
Normalized Difference Vegetative Index (NDVI) most widely used.

33. Ratio between measured reflectivity in red and near infrared.
Gives info on absorption of chlorophyll in leafy green vegetation and density of green vegetation on the surface.
Also, contrast between vegetation and soil is at a maximum.

34. Normalized Difference Vegetation Index (NDVI) has been in use for many years to measure and monitor plant growth (vigor), vegetation cover, and biomass production from multispectral satellite data.

35. NDVI is calculated from the visible and near-infrared light reflected by vegetation.
Healthy vegetation (left) absorbs most of the visible light that hits it, and reflects a large portion of the near-infrared light.
Unhealthy or sparse vegetation (right) reflects more visible light and less near-infrared light.
The numbers on the figure above are representative of actual values, but real vegetation is much more varied.

36. NDVI - ratio of red and near infrared (NIR) spectral bands :
NDVI = (NIR - red) / (NIR + red)
Resulting index value is sensitive to presence of vegetation on land surfaces and used to address vegetation type, amount, and condition.
Advanced Very High Resolution Radiometer (AVHRR).
used to generate NDVI images of large portions of Earth on regular basis to provide global images that portray seasonal and annual changes to vegetative cover.
Thematic Mapper (TM and Enhanced Thematic Mapper Plus (ETM+) bands 3 and 4 also provides Red and NIR measurements:
NDVI = (Band 4 - Band 3) / (Band 4 + Band 3)

37. AVHRR resolution is 1km and NDVI is 8 km
Primary differences between AVHRR and Landsat NDVI is resolution.
AVHRR resolution is 1km and NDVI is 8 km
Landsat NDVI resolution is 30 m
AVHRR data - frequent global NDVI products
Landsat 7 ETM+ data - greater detail covering less area.

38. NDVI equation produces values in the range of -1. 0 to 1
NDVI equation produces values in the range of -1.0 to 1.0, where vegetated areas will typically have values greater than zero and negative values indicate non-vegetated surface features such as water, barren, ice, snow, or clouds.

39. Erdas: Create NDVI Index
NDVI -1.0 to 1.0
Black values = -0.30
Whites values = 0.44

40. D. Image Classification and Analysis
Process of categorizing all pixels in an image into land cover classes.
Multispectral imagery is used.
Spectral Signatures for each pixel is the numerical basis for the algorithm.

41. Continuous data
Raster data that are quantitative (measuring a characteristic) and have related, continuous values, such as remotely sensed images (e.g., Landsat, SPOT).
Thematic data
Raster data that are qualitative and categorical.
Classes of related information, such as land cover, soil type, slope.

42. Image data classification
Primary component of image interpretation
using computer software to spectrally categorize data
computer id’s clusters of spectrally similar pixels
Analyst's knowledge
how to classify the image data
assign appropriate descriptions to the categories
Individual pixels in a continuous image are assigned to classes.
Result is a thematic image where each class represents a feature type in the real world.

43. Create thematic image from multi-spectral continuous image
Classes
DN Values

44. unsupervised - analyst may have little knowledge of what data represents. supervised - a priori knowledge required.

45. Each pixel in image contains information about the surface materials that reflected light from that pixel to the sensor.
Each pixel contains a value which can range from 0 to 255, for each band in image.

46. Vegetation - Features that are indistinguishable in
visible region of EMS
can be separated in
near IR.
NIR
VIS

48. Supervised and Unsupervised Classification
Two different approaches to classifying an image
Each has advantages and disadvantages
Unsupervised classification
primarily a computer process
minimal user input
analyst assigns an identification to each class, based on knowledge of the image's content

49. Supervised classification
user-controlled process
depends on knowledge and skills of analyst for accurate results.
analyst knows beforehand what feature classes are present and where each is in one or more locations within scene.
Used to train computer to find spectrally similar areas.

50. Unsupervised classification - used to generate a set of classes for entire image and make a preliminary interpretation.
Then supervised classification can be used to redefine the classes as more information becomes available.

51. ISODATA clustering algorithm
Unsupervised classification of remote sensing data.
Uses a minimum spectral distance formula to form clusters.
begins with arbitrary cluster means, or means of an existing signature set
each time clustering repeats, means of the clusters are shifted.
new cluster means are used for the next iteration.
Algorithm repeats the clustering of the image until either;
maximum number of iterations
maximum percentage of unchanged pixels has been reached between two iterations.

52. Ground Truthing
Verifying that feature classes derived from image data accurately represent real world features.
Requires collecting ground truth data.
Derived from a variety of sources.
onsite visits, aerial photography, maps, written reports and other sources of measurements
Ideally, should be collected at the same time as the remotely sensed data.

53. aerial photos for ground truthing.

54. Amount and type of ground truth required depends on the level of detail in the classification.
Ground truthing can be used to select training sites prior to supervised classification or to identify key classes after unsupervised classification.

55. Landsat data (resolution of 30 meters) is appropriate for classifying general landscape characteristics across large areas.

56. Uses of classification
Creation of land use and land cover (LULC) maps.
Land cover - natural and human made features: forest, grasslands, water and impervious surfaces.
Land use - how land is used: protected area, agricultural, residential, and industrial.

57. LULC classification system - widely used as a general framework.

58. LULC Maps Broad Applications: monitoring deforestation
impacts on water quality
document housing density
urban sprawl
wildlife habitat and corridors

60. Land cover classification/change detection analysis for the Columbia R
Land cover classification/change detection analysis for the Columbia R. coastal drainage area

61. Unsupervised classification
classes are determined by software based on spectral distinctions in data
little knowledge of imaged area is required
To assign identification to each class requires some knowledge of the area from personal experience or from ground truth data.
primary advantage - distinct spectral classes are identified.

62. Many of these classes might not be initially apparent to the analyst.
Spectral classes may be numerous.

63. Unsupervised
Primary disadvantage - spectral patterns identified by computer do not necessarily correspond to meaningful features of land cover or land use in the real world.

64. Supervised Classification
Classes determined by analyst.
Use pattern recognition skills and prior knowledge of the area to help software determine spectral signatures for each class.

65. Supervised classification
More accurate than unsupervised classification, provided that the classes are correctly identified by the analyst.
Disadvantage - accurately establishing the classes can be a very time-consuming process.

66. Critical part of supervised classification.
TRAINING SITES
Critical part of supervised classification.
Includes spectral characteristics for each land cover type to be classified in an image.
Software uses them to find similar areas throughout the image.
May need to establish several training sites for each class.

67. 4 training sites to establish agriculture class.

69. Unsupervised Classification - 6 Classes

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