Maa-57.2040 Kaukokartoituksen yleiskurssi

General Remote Sensing

Image enhancement I

Autumn 2007

Markus Törmä

Markus.Torma@tkk.fi

Image restoration

• Errors due to imaging process are removed

• Geometric errors– position of image pixel is not correct one when

compared to ground

• Radiometric errors– measured radiation do not correspond radiation

leaving ground

• Aim is to form faultless image of scene

Image enhancement• Image is made better suitable for interpretation• Different objects will be seen better

manipulation of image contrast and colors • Different features (e.g. linear features) will be

seen better e.g. filtering methods • Multispectral images: combination of image

channels to compress and enhance imformation– ratio images– image transformations

• Necessary information is emphasized, unnecessary removed

Image enhancement• Image is processed to be more suitable for

interpretation • Pixel operations: DN of pixel is changed

independent to other pixels– sum, multiply, subtract, ratio with constant

• Local operations: DN is changed using DN of pixels which are spatially close– filtering

• Global operations: all DNs have effect to DN– histogram manipulation– transformation to zero mean and unit deviation

HISTOGRAM• Graphical representation of probability of occurrence of image

DNs• Horizontal axis: DN from 0 to 255• Vertical axis: number of pixels with DN or probability of

occurrence of DN in image

HISTOGRAM• DNs of image are usually in narrower region that monitor can

show– usually at the darker end of scale

• DNs are scaled to larger area more DNs are used from wider region and image interpretation is enhanced

HISTOGRAM• Histogram equalization: scaling is weighted according

to probability of occurrence of DNs• More DNs are used to present commonly occurring DNs

HISTOGRAM• Nonlinearly equalized histogram: also other kinds of

mathematical functions or combinations of functions can be used

• E.g. equalized histogram should be similar to normal distribution

HISTOGRAM• Thresholding: DNs are divided to two groups• DNs less than threshold 0 • DNs more that threshold 1 • E.g. separate water areas from land areas

HISTOGRAM

"Level Slicing”

• Histogram is divided to levels• considerably less than original DNs• DNs within one level are presented using

one grey level or color• Usually used to visualize

– thermal images– vegetation index images

HISTOGRAM• Level Slicing: BW and color vesion of vegetation index image

Image filtering

• Image f is convolved with filtering mask h

g = f * h

• Image smoothing / low pass filtering: – noise removal

• Image sharpening / high pass filtering:– rapid changes in image function are enhanced

Image filtering

Image smoothing

• Random errors due to instrument noise and data transmission are removed

• Average / mean filtering

• Median filtering

Image filtering• Based on use of filtering maks h• Simple averaging filtering mask, size 5x5 pixels:

1/25 1/25 1/25 1/25 1/25

1/25 1/25 1/25 1/25 1/25

1/25 1/25 1/25 1/25 1/251/25 1/25 1/25 1/25 1/251/25 1/25 1/25 1/25 1/25

• Averaging filtering mask, size 3x3 pixels:

1/16 2/16 1/16

2/16 4/16 2/16

1/16 2/16 1/16

Image filtering

• Principle of convolution

• Filtered pixel value:

Image filtering• Original PAN and average filtered image with 3x3 filtering window

Image filtering• Original PAN and average filtered image with 7x7 filtering window

Image filtering

Median filtering

• DN of pixel is median DN of pixels defined by filtering mask

• Take pixels under filtering mask

sort from smallest to biggest

choose median (the middle one) • Useful if noise consists of single intense spikes

(removes them) and edges of areas should be preserved (do not alter them)

Image filtering

Median filtering

=> Laite taan suuruus- jä rjestykseen:

M ediaani: 35

Image filtering• Original PAN and median filtered image with 3x3 filtering window

Image filtering• Original PAN and median filtered image with 7x7 filtering window

Image filtering

• Image averaging corresponds to integration of image function

• If changes in image function are of interest derivate image function

• Image 2-dimensional function: partial derivates in x- and y-direction

• Partial derivates are used to determine amount and direction of change in each pixel

• In practice derivates are approximated by differences of neighboring pixels

• These can also be implemented using filtering masks

Image filtering

• Derivative of image function in horizontal direction can be computed using mask:

1 1 1

0 0 0

-1 -1 -1

Image filtering

• Derivative of image function in vertical direction can be computed using mask:

1 0 -1

1 0 -1

1 0 -1

Image filtering• Absolute values of partial derivative images...

Image filtering• …here magnitude of derivative is approximated by mean

of partial derivatives

Image texture• Spatial variation of image grey levels or colors• Determines smoothness or coarseness of image• Different targets have different texture can help in

interpretation • E.g. Spot panchromatic image:

– residential area: lots of variations– water: very little variations– coniferous forest: some variations

Image texture• Compute features which describes

properties of texture– new images

• In most simple case compute average value and deviation of some neighborhood– statistical properties of texture

• Some methods can take direction etc. into account– Haralick’s grey level co-occurrence matrix

Image texture• Variance and skewness of distribution, 7x7 window

Multispectral images

• Essential information from image channels

• All channels are not necessarily useful – do not use if do not need

• Some alternatives– ratio images– difference images– index images– image transformations

Visual interpretation

• Channelwise Black-and-White image

OR

• 3 channels at time, color image

Landsat-7 ETM, 29.7.2000: Visible channels blue, green, red

Infrared channels

Color image

• Humans can distinguish about 20 – 30 grey levels• Usually images have 256 grey levels

– it is not possible to distinguish small details

• Humans can distinguish millions of colors– should be exploited in interpretation

• Computers have additive color system– primary colors are Red, Green and Blue

– RGB-system

– channels are presented in combination of 3 channels

– if reflectance of target is larger in one channel than others, target is colored with that primary color

True color image • Channels are presented

using ttheir natural colors:– blue channel using blue

primary color

– green channel using green primary color

– red channel using red primary color

• Is possible with instruments with these three channels, like Landsat ETM

False color image• Channels with

wavelenghts which humans do not use or visible channels in wrong order

• E.g.:– green channel using

blue primary color– red channel using

green primary color– NIR channel using

red primary color

ETM, R: Ch7, G: Ch4, B: Ch5

IHS-color coordinates• RGB-color coordinate system is not the only one• IHS:

– Intensity: brightness of color – Hue: wavelenght of color – Saturation: purity or greyness of color

• Sometimes in order to enhance some feature, make transformation RGB IHS, edit / process image and make transformation IHS RGB

• E.g. colors to DEM– 3-channel image: RGB IHS– Change: put DEM to intensity– Make IHS RGB

IHS-color coordinates• Porvoo: ETM 321 and Intensity

IHS-color coordinates• Porvoo: ETM 321 and Hue

IHS-color coordinates• Porvoo: ETM 321 and Saturation

Ratio images

• Channel A pixel value is divided by channel B pixel value– E.g. NIR / RED

• Emphasizes the differences between channels– Increase difference between vegetated and non-

vegetated areas– Images taken at different times changes

Ratio images• If reflectances from different

targets are different, channel ratio can emphasize this difference

• E.g. Water has low reflectance at near-infrared, bigger at red

• Vegetation has low reflectance at red wavelenght, considerably bigger at near-infrared

• NIR/PUN:– Very small for water << 1– Large for vegetation >> 1

Ratio images• Multiplicative factors, which affect all channels, are

removed• Effect of topography, sun angle, shadows• Idea is to decrease the variation of DNs of pixels

belonging to same land cover• Example: CH1 CH 2 CH1/CH 2

Deciduous forest:– sun 48 50 0.96– shadow 18 19 0.95

Coniferous forest:– sun 31 45 0.69– shadow 11 16 0.69

Ratio images

• Is vegetation in good or bad condition– NIR/RED higher for vegetation is good condition

• As plant becomes ill or autunm comes– Less chlorophyll– Higher reflectance at RED wavelenghts due to

smaller chlorophyll absorption– NIR/RED smaller

Ratio images• Ratio images can be more complicated:

(CHA - CHB) / (CHC - CHB)

• It is wanted to remove some noise or atmospheric effect visible at channel B from channel ratio

Problem• In some cases different targets may look the same when

their actual reflectances differ• Can be avoided by interpreting ratio images together

with some original image channels

OIF: optimum index factor• It is easy to compute many ratio images

– Which are best?– Multispectral image, n channels: n(n-1) ratio images– Visual comparison of all combinations takes time

• OIF: best combination of three ratio images• Compute image variances and correlations between

images– Large variance: good information content– Large correlation between images: images are very much alike

• Choose three images, which– Maximize variance– Minimize correlation

Ratio image: example

• TM7 (2.2 m) / TM1 (0.48 m): sandy areas white

• TM 1.9.1990

Ratio image: example

• ETM 29.1.1999

Ratio image: example

Changes• Green: more sand

1990• Red: more sand

1999• NOTE: Images

have been taken at different seasons, so changes might be due to seasonal effects like changes in vegetation or soil moisture

Difference image

• Pixel value of channel A is subtracted from channel B value

• Image taken at time A is subtracted from image taken at time B– Changes between images

• Average filtered image is subtracted from original image– Enhances edges

Difference image• Images taken at different

times– Simple way to find out changes

• Areas without changes– Difference close to 0

• Areas with changes– Large positive / negative values

• Natural changes must be removed before change detection– Changes in illumination– Radiometric calibration and

atmospheric correction– Noise

Example: TM 191/12 20.7.1987 vs. ETM 193/11 29.7.2000, channel 3 (red)

Yellow: Ch3 reflectance has increasedRed: Ch3 reflectance has decreased

Difference image• Different channels of same image • Atmospheric or other noise is decreased

– Other channel charcterizes noise

• NIR-RED: vegetation index

Difference imageLeft: Spot5, ch2, Kolari 22.9.2009Right: Average filtered image, 5x5 window

Left: ch2 - averageRight: Absolute value of difference

Image addition• CHA + CHB

• Spatial resolution enhancement (data fusion)• Combination of image channels: spectral averaging• Gradient image + original image

– Sharpens borders can make interpretation easier

Image multiplication

• CHA * CHB

• Multiplication of two image channels increases the visual effect of topography

• Masking: unwanted areas can be removed from image – Another image is mask, where pixel is 0 if it is

to be removed– Other is image