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Remote Sensing and Image Processing: 4 Dr. Hassan J. Eghbali
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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 Dr. Hassan J. Eghbali
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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 Dr. Hassan J. Eghbali
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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 nRows (r,c) nBands (0,0) nColumns nRows (r,c) nBands Time Dr. Hassan J. Eghbali
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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 Dr. Hassan J. Eghbali
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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 1x10 6 bytes, 1MB actually 2 20 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
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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 Dr. Hassan J. Eghbali
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Image Arithmetic Combine multiple channels of information to enhance features e.g. NDVI (NIR-R)/(NIR+R) Dr. Hassan J. Eghbali
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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 Dr. Hassan J. Eghbali
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Image Arithmetic Common operators:Ratio topographic effects visible in all bands FCC Dr. Hassan J. Eghbali
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Image Arithmetic Common operators:Ratio (ch a /ch b ) apply band ratio = NIR/red what effect has it had? Dr. Hassan J. Eghbali
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Image Arithmetic Common operators:Ratio (ch a /ch b ) Reduces topographic effects Enhance/reduce spectral features e.g. ratio vegetation indices (SAVI, NDVI++) Dr. Hassan J. Eghbali
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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 Dr. Hassan J. Eghbali
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Top left AVHRR Ch3 day 235 Top Right AVHRR Ch3 day 236 Bottomdifference pseudocolur scale: black - none blue - low red - high Botswana (approximately 300 * 300km) Dr. Hassan J. Eghbali
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Image Arithmetic Common operators:Addition –Reduce noise (increase SNR) averaging, smoothing... –Normalisation (as in NDVI) + = Dr. Hassan J. Eghbali
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Image Arithmetic Common operators:Multiplication rarely used per se: logical operations? –land/sea mask Dr. Hassan J. Eghbali
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Monitoring usingVegetation Indices (VIs) Basis: Dr. Hassan J. Eghbali
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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.) Dr. Hassan J. Eghbali
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Why VIs? tracking of temporal characteristics / seasonality can can reduce sensitivity to: topographic effects (soil background) (view/sun angle (?)) (atmosphere) whilst maintaining sensitivity to vegetation Dr. Hassan J. Eghbali
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Some VIs RVI (ratio) DVI (difference) NDVI NDVI = Normalised Difference Vegetation Index i.e. combine RVI and DVI Dr. Hassan J. Eghbali
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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! Dr. Hassan J. Eghbali
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why NDVI? continuity (17 years of AVHRR NDVI) Dr. Hassan J. Eghbali
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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 Dr. Hassan J. Eghbali
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saturated Dr. Hassan J. Eghbali
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Practical 2: image arithmetic Calculate band ratios What does this show us? NDVI Can we map vegetation? How/why? Dr. Hassan J. Eghbali
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MODIS NDVI Product: 1/1/04 and 5/3/04 Dr. Hassan J. Eghbali
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