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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
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
(C) Interaction with
the Target
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
(C) Interaction with the
Target
Transmission, Reception, and Processing (E)
Interpretation and Analysis (F)
Application (G)
Reference: CCRS/CCT
Remote Sensing ProcessRemote Sensing Process
(D)Recording of Energy by the
Sensor
(A) Energy Source or
Illumination
(B)Radiation and the
Atmosphere
(C) Interaction with the
Target
Interpretation and Analysis (F)
Application (G)
Reference: CCRS/CCT
Remote Sensing ProcessRemote Sensing Process(D)
Recording of Energy by the Sensor
(E)Transmission,
Reception, and
Processing
(A) Energy Source or
Illumination
(B)Radiation and the
Atmosphere
(C) Interaction with the
Target
Interpretation and Analysis (F)
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)
Interaction with the Target (C)
Transmission, Reception, and Processing (E)
Interpretation and Analysis
(F)
(G)Application
Reference: CCRS/CCT
Recording of Energy by the
Sensor (D)
Remote Sensing ProcessRemote Sensing Process
9
AgriculturalEfficiency
Air Quality
WaterManagement
Disaster Management
CarbonManagement
Aviation
Ecological Forecasting
Invasive Species
Coastal Management
Homeland Security
Energy Management
Public Health
ApplicationsApplications
10
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
11
Types of image enhancement
Radiometric enhancement
Spatial enhancement
Spectral enhancement
12
Types of image enhancement
Radiometric enhancement
Spatial enhancement
Spectral enhancement
13
Radiometric enhancement
• 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
Radiometric enhancement
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
17
Minimum/maximum linear stretch
18
no stretch linear stretch
Minimum/maximum linear stretch
19
Contrast Stretching of Predawn Thermal Infrared Data of the Savannah River
Original
Minimum-maximum
+1 standard deviation
20
Piecewise linear stretch
Allows for enhancement of a specific range of pixel values
21
Piecewise linear stretch
• Slope of the linear contrast
enhancement changes
• Piecewise contrast stretching
(sometimes referred to as
using breakpoints)
Piecewise Linear Contrast Stretching
23
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
24
Histogram equalization
Dark LightMost populated
25
Histogram matching
Convert the histogram of one image to match the
histogram of another
26
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
27
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
29
Types of image enhancement
Radiometric enhancement
Spatial enhancement
Spectral enhancement
30
Spatial enhancement
• Modifies pixel values based on the values of surrounding
pixels
• Changes the “spatial frequency” of an image
31
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
32
Neighboring pixel brightness values rather than an independent pixel value
Spatial frequency
33
Types of spatial enhancement
1. Convolution filtering
2. Resolution merge
34
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
35
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)
36
Kernel
37
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
38
= [(-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
39
• 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
40
High-frequency (high-pass) kernel
before filtering after filtering
41
• 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
42
Zero-sum kernel
before filtering after filtering
43
• 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
44
Low-frequency (low-pass) kernel
before filtering after filtering
45
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”)
46
Resolution merge
+ =
original MS (30m) panchromatic (15m) output image (15m)
note that this changes the input image pixel values
47
Types of image enhancement
Radiometric enhancement
Spatial enhancement
Spectral enhancement
48
Spectral enhancement
• 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
49
Types of spectral enhancement
1. Principal component analysis
2. Tasseled cap
3. Indices
50
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
51
Principal Components Analysis (PCA)
• 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
7
3
4 5
Landsat ETM+ (6 bands, excluding thermal & pan)
54
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
Principal Components Analysis (PCA)
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
55
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
57
58
Indices
• Create new images by mathematically combining the
pixel values from multiple image bands
• Most often ratios of band values
59
Common uses of indices
Mineral exploration
Reduce radiometric differences
Minimize shadow effects
Vegetation analysis
60
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
61
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)
63
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)
Normalized Difference Vegetation Index
(NDVI)
The main difference between green and dry vegetation is the amount of red visible absorbed
65
greyscale NDVI pseudocolor NDVI
Normalized Difference Vegetation Index
(NDVI)
NASA MODIS global NDVI