remote sensing and image processing: 4 dr. hassan j. eghbali
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Remote Sensing and Image Processing: 4
Dr. Hassan J. Eghbali
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
Dr. Hassan J. Eghbali
Basic image characteristics
• pixel - DN
• pixels - 2D grid (array)
• rows / columns (or lines / samples)
• dynamic range
– difference between lowest / highest DN
Dr. Hassan J. Eghbali
• 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
Dr. Hassan J. Eghbali
• Several ways to arrange data in binary image file– Band sequential (BSQ)
– Band interleaved by line (BIL)
– Band interleaved by pixel (BIP)
Aside
Dr. Hassan J. Eghbali
• 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
Dr. Hassan J. Eghbali
• 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
Dr. Hassan J. Eghbali
Image Arithmetic• Combine multiple
channels of information to enhance features
• e.g. NDVI
(NIR-R)/(NIR+R)
Dr. Hassan J. Eghbali
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
Dr. Hassan J. Eghbali
Image Arithmetic
• Common operators: Ratio
topographic effects
visible in all bands
FCC
Dr. Hassan J. Eghbali
Image Arithmetic
• Common operators: Ratio (cha/chb)
apply band ratio
= NIR/red
what effect has it had?
Dr. Hassan J. Eghbali
Image Arithmetic
• Common operators: Ratio (cha/chb)
• Reduces topographic effects
• Enhance/reduce spectral features
• e.g. ratio vegetation indices (SAVI, NDVI++) Dr. Hassan J. Eghbali
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
Dr. Hassan J. Eghbali
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)
Dr. Hassan J. Eghbali
Image Arithmetic• Common operators: Addition
– Reduce noise (increase SNR) • averaging, smoothing ...
– Normalisation (as in NDVI)
+
=
Dr. Hassan J. Eghbali
Image Arithmetic
• Common operators: Multiplication
• rarely used per se: logical operations?– land/sea mask
Dr. Hassan J. Eghbali
Monitoring usingVegetation Indices (VIs)
• Basis:
Dr. Hassan J. Eghbali
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.)
Dr. Hassan J. Eghbali
Why VIs?
tracking of temporal characteristics / seasonality
cancan reduce sensitivity to: topographic effects (soil background) (view/sun angle (?)) (atmosphere)
whilst maintaining sensitivity to vegetation
Dr. Hassan J. Eghbali
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
Dr. Hassan J. Eghbali
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!
Dr. Hassan J. Eghbali
why NDVI?
continuity (17 years of AVHRR NDVI)
Dr. Hassan J. Eghbali
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
Dr. Hassan J. Eghbali
Dr. Hassan J. Eghbali
saturated
Dr. Hassan J. Eghbali
Practical 2: image arithmetic
Calculate band ratios What does this show us?
NDVI Can we map vegetation? How/why?
Dr. Hassan J. Eghbali
MODIS NDVI Product: 1/1/04 and 5/3/04
Dr. Hassan J. Eghbali