remote sensing and image processing: 4
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Remote Sensing and Image Processing: 4
Dr. Mathias (Mat) Disney
UCL Geography
Office: 301, 3rd Floor, Chandler House
Tel: 7670 4290
Email: mdisney@geog.ucl.ac.uk
www.geog.ucl.ac.uk/~mdisney
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Image display and enhancement
Purpose
• visual enhancement to aid interpretation
• enhancement for improvement of information extraction techniques
• Today we’ll look at image arithmetic and spectral indices
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Basic image characteristics
• pixel - DN
• pixels - 2D grid (array)
• rows / columns (or lines / samples)
• dynamic range
– difference between lowest / highest DN
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• Size of digital image data easy (ish) to calculate– size = (nRows * nColumns * nBands * nBitsPerPixel) bits
– in bytes = size / nBitsPerByte
– typical file has header information (giving rows, cols, bands, date etc.)
Aside: data volume?
(0,0)nColumns
nRow
s
(r,c)
nBands(0,0)
nColumns
nRow
s
(r,c)
nBands
Time
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• Several ways to arrange data in binary image file– Band sequential (BSQ)
– Band interleaved by line (BIL)
– Band interleaved by pixel (BIP)
Aside
From http://www.profc.udec.cl/~gabriel/tutoriales/rsnote/cp6/cp6-4.htm
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• Landsat ETM+ image? Bands 1-5, 7 (vis/NIR)– size of raw binary data (no header info) in bytes?
– 6000 rows (or lines) * 6600 cols (or samples) * 6 bands * 1 byte per pixel = 237600000 bytes ~ 237MB
• actually 226.59 MB as 1 MB 1x106 bytes, 1MB actually 220 bytes = 1048576 bytes
• see http://www.matisse.net/mcgi-bin/bits.cgi
– Landsat 7 has 375GB on-board storage (~1500 images)
Data volume: examples
Details from http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbook_htmls/chapter6/chapter6.htm
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• MODIS reflectance 500m tile (not raw swath....)?– 2400 rows (or lines) * 2400 cols (or samples) * 7 bands * 2 bytes per
pixel (i.e. 16-bit data) = 80640000 bytes = 77MB
– Actual file also contains 1 32-bit QC (quality control) band & 2 8-bit bands containing other info.
• BUT 44 MODIS products, raw radiance in 36 bands at 250m
• Roughly 4800 * 4800 * 36 * 2 ~ 1.6GB per tile, so 100s GB data volume per day!
Data volume: examples
Details from http://edcdaac.usgs.gov/modis/mod09a1.asp and http://edcdaac.usgs.gov/modis/mod09ghk.asp
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Image Arithmetic• Combine multiple
channels of information to enhance features
• e.g. NDVI
(NIR-R)/(NIR+R)
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Image Arithmetic
• Combine multiple channels of information to enhance features
• e.g. Normalised Difference Vegetation Index (NDVI)– (NIR-R)/(NIR+R) ranges between -1 and 1– Vegetation MUCH brighter in NIR than R so NDVI for veg. close to 1
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Image Arithmetic
• Common operators: Ratio
topographic effects
visible in all bands
FCC
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Image Arithmetic
• Common operators: Ratio (cha/chb)
apply band ratio
= NIR/red
what effect has it had?
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Image Arithmetic
• Common operators: Ratio (cha/chb)
• Reduces topographic effects
• Enhance/reduce spectral features
• e.g. ratio vegetation indices (SAVI, NDVI++)
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Image Arithmetic
• Common operators: Subtraction
• examine CHANGE e.g. in land cover
An active burn near the Okavango Delta, Botswana
NOAA-11 AVHRR LAC data (1.1km pixels)
September 1989.
Red indicates the positions of active fires
NDVI provides poor burned/unburned discrimination
Smoke plumes >500km long
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Top left AVHRR Ch3 day 235
Top Right AVHRR Ch3 day 236
Bottom difference
pseudocolur scale:
black - none
blue - low
red - high
Botswana (approximately 300 * 300km)
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Image Arithmetic• Common operators: Addition
– Reduce noise (increase SNR) • averaging, smoothing ...
– Normalisation (as in NDVI)
+
=
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Image Arithmetic
• Common operators: Multiplication
• rarely used per se: logical operations?– land/sea mask
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Monitoring usingVegetation Indices (VIs)
• Basis:
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Why VIs?• empirical relationships with range of vegetation /
climatological parameters fAPAR – fraction of absorbed photosynthetically active
radiation (the bit of solar EM spectrum plants use) NPP – net primary productivity (net gain of biomass by
growing plants)
simple (understand/implement) fast (ratio, difference etc.)
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Why VIs?
tracking of temporal characteristics / seasonality
cancan reduce sensitivity to: topographic effects (soil background) (view/sun angle (?)) (atmosphere)
whilst maintaining sensitivity to vegetation
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Some VIs
• RVI (ratio)
• DVI (difference)
• NDVI
RVI nir
red
DVI nir red
NDVI nir red
nir red
NDVI = Normalised Difference Vegetation Index i.e. combine RVI and DVI
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Properties of NDVI? Normalised, so ranges between -1 and +1
If NIR >> red NDVI 1
If NIR << red NDVI -1
In practice, NDVI > 0.7 almost certainly vegetation
NDVI close to 0 or slightly –ve definitelyy NOT vegetation!
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why NDVI?
continuity (17 years of AVHRR NDVI)
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limitations of NDVI NDVI is empirical i.e. no physical meaning atmospheric effects:
esp. aerosols (turbid - decrease) direct means - atmospheric correction indirect means: atmos.-resistant VI
(ARVI/GEMI) sun-target-sensor effects (BRDF):
MVC ? - ok on cloud, not so effective on BRDF saturation problems:
saturates at LAI of 2-3
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saturated
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Practical 2: image arithmetic
Calculate band ratios What does this show us?
NDVI Can we map vegetation? How/why?
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MODIS NDVI Product: 1/1/04 and 5/3/04
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