Image Enhancements, Indices and Transformations. (A) Energy Source or Illumination Radiation and the...

Image Enhancements, Indices and

Transformations

Image Enhancements, Indices and

Transformations

(A) Energy Source or Illumination

Radiation and the Atmosphere (B)

Interaction with the Target (C)

Transmission, Reception, and Processing (E)

Interpretation and Analysis (F)

Application (G)

Reference: CCRS/CCT

Recording of Energy by the Sensor (D)

Remote Sensing ProcessRemote Sensing Process

(A) Energy Source or

Illumination

(B)Radiation and the

Atmosphere




Application (G)

Reference: CCRS/CCT



(A) Energy Source or Illumination


Atmosphere

(C) Interaction with

the Target



Application (G)

Reference: CCRS/CCT




Illumination


Atmosphere

(C) Interaction with the

Target



Application (G)

Reference: CCRS/CCT


(D)Recording of Energy by the

Sensor


Illumination


Atmosphere


Target


Application (G)

Reference: CCRS/CCT

Remote Sensing ProcessRemote Sensing Process(D)

Recording of Energy by the Sensor

(E)Transmission,

Reception, and

Processing


Illumination


Atmosphere


Target


Reference: CCRS/CCT

Remote Sensing ProcessRemote Sensing Process(D)

Recording of Energy by the Sensor

(E)Transmission, Reception,

and Processing

(F)Interpretation and

Analysis

Energy Source or Illumination

(A)

Radiation and the

Atmosphere (B)



Interpretation and Analysis

(F)

(G)Application

Reference: CCRS/CCT

Recording of Energy by the

Sensor (D)


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AgriculturalEfficiency

Air Quality

WaterManagement

Disaster Management

CarbonManagement

Aviation

Ecological Forecasting

Invasive Species

Coastal Management

Homeland Security

Energy Management

Public Health

ApplicationsApplications

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Image enhancement

Alteration of the image in such a way that the information

contained in the image is easier to visually interpret or

systematically analyze

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Types of image enhancement

Radiometric enhancement

Spatial enhancement

Spectral enhancement

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Spatial enhancement


13


• Compensates for inadequacies in the image contrast

(too dark, too bright, too little difference between the

brightness of features in the image)

• Attempts to optimize the distribution of pixel values over

the radiometric range of the image

14


Often increases contrast for some image pixels while

decreasing it for others.

15

Types of radiometric enhancement

1. Linear stretch

2. Piecewise linear stretch

3. Histogram equalization (non-linear stretch)

16

Linear stretch

• Simple method that expands the range of original image

pixel values to the full radiometric range of the image;

• Best applied to images where pixel values are normally

distributed

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Minimum/maximum linear stretch

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no stretch linear stretch

Minimum/maximum linear stretch

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Contrast Stretching of Predawn Thermal Infrared Data of the Savannah River

Original

Minimum-maximum

+1 standard deviation

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Piecewise linear stretch

Allows for enhancement of a specific range of pixel values

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Piecewise linear stretch

• Slope of the linear contrast

enhancement changes

• Piecewise contrast stretching

(sometimes referred to as

using breakpoints)

Piecewise Linear Contrast Stretching

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Histogram equalization (non-linear stretch)

• Redistributes pixel values so that there are roughly the

same number of pixels with each value within a range

• Applies greatest contrast enhancement at the peaks of

the histogram

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Histogram equalization

Dark LightMost populated

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Histogram matching

Convert the histogram of one image to match the

histogram of another

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Histogram matching rules

General shape of histograms should be similar

Relative dark/light features should be the same

Spatial resolution should be the same

Same relative distribution of land cover

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Histogram matching rules

• Histogram matching is useful for matching data of the

same or adjacent scenes that were scanned on separate

days, or are slightly different because of sun angle or

atmospheric effects

• Especially useful for mosaicing or change detection

28

Histogram matching

+ =

input image match image LUT output image

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Spatial enhancement


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Spatial enhancement

• Modifies pixel values based on the values of surrounding

pixels

• Changes the “spatial frequency” of an image

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Spatial frequency

• The number of changes in pixel value per unit distance for

any particular part of an image

• Few changes – low frequency area

• Dramatic changes – high frequency area

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Neighboring pixel brightness values rather than an independent pixel value

Spatial frequency

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Types of spatial enhancement

1. Convolution filtering

2. Resolution merge

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Convolution filtering

• Process of assigning a new value for an image pixel

based on a weighted average of surrounding pixels

• Can be used to visually enhance an image OR to prepare

an image for classification

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Kernel

• A matrix of coefficients used to average the value of

each image pixel with the neighborhood of pixels

surrounding it

• Kernel is systematically moved across the image and a

new value is calculated for each input image pixel (at

the center of the kernel)

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Kernel

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Convolution Formula

V

f ijdijj1

q

i1

q

F

where :

f ij

dij

qF V

the kernel coefficient at column i,row j

the pixel value at column i, row j

the dimension of the kernel (i.e., 3X3)

the sum of the kernel coefficients (if 0, then 1)

the output pixel value

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= [(-1 × 8) + (-1 × 6) + (-1 × 6) + (-1 × 2) + (16 × 8) + (-1 × 6) + (-1 × 2) + (-1 × 2) + (-1 × 8)] / (-1 + -1 + -1 + -1 + 16 + -1 + -1 + -1 + -1)= (-8 + -6 + -6 + -2 + 128 + -6 + -2 + -2 + -8) / 8= 88 / 8= 11

Convolution Formula

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• Increase spatial frequency

• used to enhance “edges” between

non-homogeneous groups of image

pixels

• Not often used prior to classification

-1 -1 -1

-1 9 -1

-1 -1 -1

High-frequency (high-pass) kernel

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High-frequency (high-pass) kernel

before filtering after filtering

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• Sum of all kernel coefficients is zero

• Output pixel values are zero where

equal

• Low values become much lower, high

values become much higher

• Used as an edge detector

• Can be biased to detect edges in a

certain direction

• Kernel above is biased towards the

south

• Stream delineation, fault mapping

-1 -1 -1

1 -2 1

1 1 1

Zero-sum kernel

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Zero-sum kernel


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• Kernel coefficients are usually equal

• Simply averages pixel values

• Results in increased pixel

homogeneity and a “smoother” image

• Most widely-used filtering mechanism

• Smooth terrain; reduce noise;

generalize land cover (post-

classification)

• Kernel: 3X3 or 5X5

1 1 1

1 1 1

1 1 1

Low-frequency (low-pass) kernel

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Low-frequency (low-pass) kernel


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Resolution merge

Using an image with high spatial resolution to increase the

spatial resolution of a lower spatial resolution image of the

same area (a.k.a., “pan sharpening”)

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Resolution merge

+ =

original MS (30m) panchromatic (15m) output image (15m)

note that this changes the input image pixel values

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Spatial enhancement


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• Create, expand, transform, analyze or compress multiple

bands of image data

• Can be used to both visually enhance data and prepare

it for image classification

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Types of spectral enhancement

1. Principal component analysis

2. Tasseled cap

3. Indices

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Principal Components Analysis (PCA)

• Transforms a multi-band image into a series of

uncorrelated images (“components”) that represent

most of the information present in the original dataset

• Can be more useful for analysis than the original source

data

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• The first one or two components represent most of the

information (variance) present in the original image

bands; PCA reduces data redundancy

• First PC accounts for the maximum proportion of the

variance, each succeeding PC accounts for the

maximum proportion of the remaining variance

• Reduce dimensionality (i.e., # of bands need to be

analyzed)

band #1 values

ban

d #

2 v

alu

es

1st component is the longest axis (AB); minimizes the squared distance from each point to the line

2nd component is the 2nd longest axis (CD); it’s “orthagonal”, or completely uncorrelated with the first axis

Original image values are converted based on the equation defining the axis line

1 2

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3

4 5

Landsat ETM+ (6 bands, excluding thermal & pan)

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pixel 1 pixel 2 pixel 3

band 1 40 60 100

band 2 60 80 120

band 3 30 50 90

band 4 200 30 50

band 5 120 230 20

band 7 10 80 255


PCA seeks to generate uncorrelated images to reduce data

redundancy.

• The pixels in bands 1 through 3 are perfectly

correlated

• Band 2 = Band 1 + 20; Band 3 = Band 1 -10

• Bands 4, 5 & 7 are less correlated to Band 1. They contain

more unique information to contribute to the first PC

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Tasseled cap transformation

• Transforms a multi-band image into a series of images

optimized for vegetation studies using coefficients specific to

a particular sensor

• Images represent the “brightness”, “greenness”, and

“wetness”

• Vegetation studies:

brightness is used to identify and measure soil

greenness is used to identify and measure vegetation

wetness is used to measure soli/vegetation moisture

content

Micale and Marrs 2006

Healthy dense vegetation

Bare soil

Water

Tasseled cap transformation

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Indices

• Create new images by mathematically combining the

pixel values from multiple image bands

• Most often ratios of band values

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Common uses of indices

Mineral exploration

Reduce radiometric differences

Minimize shadow effects

Vegetation analysis

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Normalized Difference Vegetation Index (NDVI)

• A ratio of the red visible and near infrared bands

• Used widely as a measure of both the presence and

health of vegetation

• Values range from -1 to +1

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Normalized Difference Vegetation Index (NDVI)

Based upon findings that the chlorophyll in plant leaves

strongly absorbs red visible light (from 0.6 to 0.7 µm), while

the cell structure of the leaves strongly reflects near-

infrared light (from 0.7 to 1.1 µm)

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pp

ppp RNIR

RNIRNDVI

where:NIR is the near-infrared response of pixel p R is the visible red response of pixel p

Normalized Difference Vegetation Index

example: NIR=100, R=50 (0.333)

NDVI is positive when NIR > R, negative when NIR < R

Larger NDVI values result from larger differences between the NIR and Red bands

Note that the software may scale the -1 to +1 NDVI values to 8-bit (0 to 255)


(NDVI)

The main difference between green and dry vegetation is the amount of red visible absorbed

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greyscale NDVI pseudocolor NDVI


(NDVI)

NASA MODIS global NDVI

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