4.3 Digital Image Processing. Common image processing image analysis functions: A. Preprocessing B. Image Enhancement C. Image Transformation D. Image Classification and Analysis. Image Transformations. Manipulation of multiple bands of data Generates a ‘new’ image 1. 3 band combinations
Common image processing image analysis functions:
B. Image Enhancement
C. Image Transformation
D. Image Classification and Analysis
1. 3 band combinations
2. Spectral ratioing(arithmetic operations)
3 0.63 to 0.69 RedMatches chlorophyll absorption-used for discriminating vegetation types.
4 0.76 to 0.90 Near IRUseful for determining biomass content.
6 10.40 to 12.50 Thermal IR. Geological mapping, soil moisture, Thermal pollution monitoring and ocean current studies.
7 2.08 to 2.35 Short Wave IRRatios of 5 & 7 are used to map mineral deposits.
Bands 4, 3, 2
Near Infrared Composite (4,3,2)
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.
In a SWIR composite, sensor band 7 is selected from the short-wave
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.
BlueGreen Red Near IR
BlueGreen Red Near IR
Blue Green Red Near IR
2. Spectral ratioing - Using vegetation indices such as NDVI to study vegetation
10.45 to 0.52 Blue
20.52 to 0.60 Green
3 0.63 to 0.69 Red
4 0.76 to 0.90 Near IR
51.55 to 1.75 Short Wave IR
6 10.40 to 12.50 Thermal IR.
7 2.08 to 2.35 Short Wave IR
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.
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.
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.
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.
Erdas: Create NDVI Index
NDVI -1.0 to 1.0
Black values = -0.30
Whites values = 0.44
Create thematic image from multi-spectral continuous image
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.
Features that are
visible region of EMS
can be separated in
4 training sites to establish agriculture class.