remote sensing and image processing: 4 dr. hassan j. eghbali

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