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16 Cards in this Set
- Front
- Back
- 3rd side (hint)
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Radiometric correction of topographic effects |
A digital elevation model is required - cosine correction: Lh= Lt (cos sun angle/ cos of incidence between normal and sun) Lh= radiance or brightness for horizontal surface Lt= radiance over sloped surface Cos sun angle can be determined by time of day |
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Geometric correction |
Process of transforming image data so as to create an image oriented to map coordinates in a specific map projection |
Sources of geometric are: aerial photography, push broom or whisk broom Airborne: altitude variation, velocity variation (altitude: pitch, row, yaw) Satellite: earth rotation, Earth curvature |
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Types of geometric errors |
Systematic: removed at satellite ground stations Nonsystematic: use polynomial equation |
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Radiometric enhancement |
Adjusting histogram of a single band to stretch it from 0 to 255 |
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Spatial enhancement |
Neighbor pixels are used to revise 0 or 255 center pixel New image will have bad values removed (smoothed) Smooth filter- Blurs contrasting features Edge filter- enhances contrasting features |
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Spectral enhancement (PCA) |
-squeeze all information from 7 bands and two fewer bands -bands tend to be correlated with each other -redundant information compression, save useful information, and scrap the rest |
Only works with high correlations between bands. You want pixels set as far apart from each other as possible because it gives you more information (variance) |
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Vegetation indices |
Pigment chlorophyl a and b Chlorophyll a peak absorption at 0.43 and 0.66 nanometers Chlorophyll b peak absorption at 0.45 and 0.65 nanometers In the fall chlorophyll lowers Spongy mesophyll scattering effect in 0.7 to 1.3 nanometers (infared) Water and leaf absorbs at two dips (entire area 1.4 to 2.6 nanometers) |
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Simple ratio |
SR= NIR/RED => first true vegetation index |
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Normalize difference vegetation index (NDVI) |
NDVI = NIR - RED/NIR + RED Estimating net primary production over varying biomes Can range from 0,1 to -1,1 Most of the time greater than zero which is closer to 1 => more or dense vegetation Healthy: red= 0.2 NIR= 0.7 Stressed: red= 0.3 NIR= 0.5 |
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PRI, photochemical reflectance index |
Ability of plant to do photosynthesis |
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KT transformation |
"Tassled Cap" Very useful for agriculture more than ndvi Greeness as y axis, brightness as x Tassled Cap shape |
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Texture transformations |
Example urban areas have a different texture. Spectral reflectance will be different based on road, grass or building etc |
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Image classification |
Land cover-type of material present Land use Classification scheme Level 1: forest, agriculture, urban Level 2: conifer or broadleaf, corn or soybean, high density low density |
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Unsupervised classification |
ISO data-interative self-organizing data interpretive means repeating the same process getting more info each time with the best results of classification at the end |
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Supervised classification |
-need training samples -training data improve accuracy (go out to filled with GPS) 10n: n is number of bands Example landsat 7: 70 samples per class (forest, agric, urban, etc) Use PCA to reduce number of bands |
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First order polynomial |
Geometric correction U= Ao + A1x + A2y V= Bo + B1x + B2y x, y: image coordinates 3 ground control points needed to find out those variables -the more the better, but means you have to travel to more GPS pts |
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