GNR607 Principles of Satellite Image Processing Instructor: Prof. - - PowerPoint PPT Presentation

GNR607 Principles of Satellite Image Processing

Instructor: Prof. B. Krishna Mohan CSRE, IIT Bombay bkmohan@csre.iitb.ac.in

Slot 2 Lecture 32-34 Principal Component Transform and Band Arithmetic October 14, 2014 10.35 AM – 11.30 AM October 16, 2014 11.35 AM – 12.30 PM, 3.30 – 5.00 PM

Decorrelation Stretch

IIT Bombay Slide 47 GNR607 Lecture 32-34 B. Krishna Mohan

Decorrelation Stretch

IIT Bombay Slide 47a GNR607 Lecture 32-34 B. Krishna Mohan

• Variance of lower order principal

components is low

• Apply enhancement to these lower order

PCs

• Apply Inverse PCT (discussed next)
• Form color composites (FCC, True color

composites)

• See improvement in visual quality

IIT Bombay Slide 47b GNR607 Lecture 32-34 B. Krishna Mohan ASTER Satellite Image Enhancement Source:

http://www.gisdevelopment.net/technology/rs/techrs0023a.htm

IIT Bombay Slide 47c GNR607 Lecture 32-34 B. Krishna Mohan Source: http://www.dstretch.com/AlgorithmDescription.html Burham Canyon (KER-273) Enhancement of Rock Art Paintings

Inverse PCT

IIT Bombay Slide 48 GNR607 Lecture 32-34 B. Krishna Mohan

• Inverse PCT is used to generate the

bands in the original domain

• If ALL PCTs are retained, inverse will give

back the original bands

• If any PCTs are dropped, inverse will give

new bands in the original domain that may be close to the original bands depending

• n how many PCTs are discarded

Inverse PCT

IIT Bombay Slide 49 GNR607 Lecture 32-34 B. Krishna Mohan

From the principle of PCT, we have y = Dtx Dt contains eigenvectors of Sx, covariance matrix from the original image. D has eigenvectors as columns, thus Dt has the eigenvectors as rows Since Dt is an orthonormal matrix, Dt .D = I (each row is orthogonal to other rows) (Dt)t = (Dt)-1 From each pixel vector in PC domain, x = (Dt)t y

Inverse PCT

IIT Bombay Slide 50 GNR607 Lecture 32-34 B. Krishna Mohan

For k band image, matrix D is square, of size k x k If m principal components are dropped, we are left with a matrix (D1) of size k x (k-m) The vector y is reduced to y1 of size k-m x 1 Therefore the modified vector x1 is given by x1 = D1y1 The difference between x and x1 is a measure of the loss of information due to removal of some

• f the PCs

Comments on PCT

IIT Bombay Slide 51 GNR607 Lecture 32-34 B. Krishna Mohan

• One of the other important applications of

PCT is data fusion

• Images from two sensors can be fused to

produce a new image that has the strong points of both the input images

• PCT based fusion is a well known

approach

Data Fusion

Data Fusion

• Combine datasets to prepare a superior

dataset

• Stack up all the datasets to create a large

higher dimensional dataset – e.g., multitemporal data from same sensor

• Fuse the datasets to create a higher

resolution dataset

• Fuse the datasets to create a new dataset

that has attributes of individual ones

GNR607 Lecture 32-34 B. Krishna Mohan IIT Bombay Slide 52

Data Fusion

• Most commonly employed by endusers of

remotely sensed data

• Supported by most software packages

GNR607 Lecture 32-34 B. Krishna Mohan IIT Bombay Slide 53

Introduction

• Merging multi-sensor data can help exploit

strengths of various data sets

– Radiometric resolution advantage – Spatial resolution advantage – Spectral resolution advantage – Temporal resolution advantage

GNR607 Lecture 32-34 B. Krishna Mohan IIT Bombay Slide 54

Spatial Resolution Enhancement

• This is the most common application of

data fusion

– Low resolution images have fewer pixels per unit area due to larger pixel size – Improve spatial resolution – High resolution images provide more pixels per unit area by smaller sampling interval (pixel size)

GNR607 Lecture 32-34 B. Krishna Mohan IIT Bombay Slide 55

Zooming is NOT resolution enhancement

• How is spatial resolution enhanced?
• Low resolution  absence of high spatial

frequency content

• High frequency information is to be

transferred from another data source (of higher resolution)

GNR607 Lecture 32-34 B. Krishna Mohan IIT Bombay Slide 56

Resolution Sharpening

• Most often, data from the lower spatial

resolution multispectral sensors and the higher spatial resolution panchromatic sensors are merged

• Results in multispectral data at higher

spatial resolution

GNR607 Lecture 32-34 B. Krishna Mohan IIT Bombay Slide 57

Multi-sensor Data Merging

Most common operation

• PAN images to sharpen multispectral data

e.g., IRS pan + IRS ms

• Sharpening low resolution multispectral

images with high resolution multispectral images For instance, SPOT ms + TM ms (20 metres) (30 metres)

GNR607 Lecture 32-34 B. Krishna Mohan IIT Bombay Slide 58

Input Image Preparation

• Contrast Adjustment

– Zoom low resolution image to the same physical size of the high resolution image – Match histogram of the MS image with that of PAN image using histogram based techniques

• Image Registration

– Register the zoomed low resolution image to the high resolution image. This should be accurate to a fraction of a pixel

GNR607 Lecture 32-34 B. Krishna Mohan IIT Bombay Slide 59

Image Sharpening

• MShr = f(MSlr, PANhr) , where
• MS = multispectral Image
• PAN = Panchromatic Image
• lr = low resolution
• hr = high resolution

GNR607 Lecture 32-34 B. Krishna Mohan IIT Bombay Slide 60

Sharpening Techniques

• Principal Component Analysis method
• Intensity-Hue-Saturation method
• Ratio-based (Brovey Transform)
• Arithmetic algorithm
• Multiplicative
• Wavelet Transform method

GNR607 Lecture 32-34 B. Krishna Mohan IIT Bombay Slide 61

PCA Merge

• The 1st PC is most influential
• It should be used while merging so that the

effect is felt on all bands

GNR607 Lecture 32-34 B. Krishna Mohan IIT Bombay Slide 62

PCA Merge

• The 1st principal component is replaced by

the high resolution image

• Inverse PCT is applied

GNR607 Lecture 32-34 B. Krishna Mohan IIT Bombay Slide 63

Results

• This technique is useful to transform all

bands at a time

• Often works well in producing good fusion

results

GNR607 Lecture 32-34 B. Krishna Mohan IIT Bombay Slide 64

PCA Merge

• Very effective when the correlation

between PAN image and the multispectral image is good

• Does not work very well when fusion is

done with images from different types of sensors such as SAR and optical

GNR607 Lecture 32-34 B. Krishna Mohan IIT Bombay Slide 65

Input High Resolution Image

GNR607 Lecture 32-34 B. Krishna Mohan IIT Bombay Slide 66

Sample Eigenvectors and Eigenvalues

IIT Bombay Slide 30 GNR607 Lecture 32-34 B. Krishna Mohan 34.89 55.62 52.87 22.71 55.62 105.95 99.58 43.33 52.87 99.58 104.02 45.80 22.71 43.33 45.80 21.35 Covariance Matrix

0.34 −0.61 0.71 −0.06

0.64 −0.40 −0.65 −0.06 0.63 0.57 0.22 0.48 0.28 0.38 0.11 −0.88 Eigenvalues 253.44 7.91 3.96 0.89 Eigenvectors

Sample Eigenvectors and Eigenvalues

IIT Bombay Slide 31 GNR607 Lecture 32-34 B. Krishna Mohan

Input Multispectral Image

GNR607 Lecture 32-34 B. Krishna Mohan IIT Bombay Slide 67

PCT Resolution Merge

IIT Bombay Slide 68 GNR607 Lecture 32-34 B. Krishna Mohan

RGB-HSI Transform Method

• In color images, the spectral information is

contained in the hue and the saturation.

• Hue denotes the basic dominant wavelength of

the radiation

• Saturation denotes the purity of the color or is a

function of the amount of dilution of the color with white light

• Intensity is an indicator of the strength of the

color or the magnitude of the energy that reaches our eye

GNR607 Lecture 32-34 B. Krishna Mohan IIT Bombay Slide 69

RGB-HSI Transform Method

• The philosophy in HSI based fusion is to replace

the intensity with the new data set first and then compute the inverse transform of the HSI data set to the RGB coordinate system

• The spatial resolution of the added component

and the spectral information in the hue and saturation together provide an enhanced data set compared to the original low resolution multispectral and high resolution panchromatic data sets.

GNR607 Lecture 32-34 B. Krishna Mohan IIT Bombay Slide 70

RGB-HSI Transform Method

Algorithm:

– Choose any three bands of the multispectral input data set; denote them by the Red, Green and Blue coordinates respectively. – Transform the RGB data to IHS color space. – Replace the Intensity component with PAN image. – Perform an inverse IHS to RGB color space.

• Good results are obtained for visualization
• Limited to only three bands

GNR607 Lecture 32-34 B. Krishna Mohan IIT Bombay Slide 71

IHS Resolution Merge

GNR607 Lecture 32-34 B. Krishna Mohan IIT Bombay Slide 72

IHS Resolution Merge - FCC

GNR607 Lecture 32-34 B. Krishna Mohan IIT Bombay Slide 73

Band Arithmetic

Motivation

IIT Bombay Slide 74 GNR607 Lecture 32-34 B. Krishna Mohan

Multiband Arithmetic

IIT Bombay Slide 75 GNR607 Lecture 32-34 B. Krishna Mohan

• In a given pair of bands the response of two
• bjects is generally different.
• Pixel by pixel comparison between images can

highlight pixels that have very high difference in reflectance in those bands

• Operations like band difference and band ratio
• r combinations of them are popularly used for

this purpose

Band Ratio

• Very common operation

Ratioi,j(m,n) = Bandi (m,n) / Bandj(m,n) If Bandj(m,n) = 0, suitable adjustment has to be made (e.g., add +1 to the denom.) Minimum ratio will be 0; Maximum ratio will be 255

IIT Bombay Slide 76 GNR607 Lecture 32-34 B. Krishna Mohan

Input Image

IIT Bombay Slide 77 GNR607 Lecture 32-34 B. Krishna Mohan

Input Image FCC

IIT Bombay Slide 78 GNR607 Lecture 32-34 B. Krishna Mohan

IR/R

IIT Bombay Slide 79 GNR607 Lecture 32-34 B. Krishna Mohan

Band Ratio

• For fast computing, approximations can be

made such as: 0 ≤ Ratioi,j(m,n) ≤1, Ratioi,j(m,n)scaled = Round [Ratioi,j(m,n)x127] 1 < Ratioi,j(m,n) ≤ 255, Ratioi,j(m,n)scaled = Round [127 + Ratioi,j(m,n)/2]

• Advantage – in one pass image is generated in

range 0-255

IIT Bombay Slide 80 GNR607 Lecture 32-34 B. Krishna Mohan

Band Difference

• Similar to band ratio, band difference can

also be used to account for difference in reflectance by objects in two wavelengths

• Band ratio - more popular in practical

applications such as geological mapping

• Topographic effects on the images are

reduced by ratioing.

IIT Bombay Slide 81 GNR607 Lecture 32-34 B. Krishna Mohan

Band Multiplication

• Pixel by pixel multiplication of two images
• Not used to multiply gray levels in one

band with corresponding gray levels in another band

• Used in practice to mask some part of the

image and retain the rest of it by preparing a mask image and performing image to image multiplication of pixels

IIT Bombay Slide 82 GNR607 Lecture 32-34 B. Krishna Mohan

Band Multiplication

Mask Image Input Image White=1, Black=0

IIT Bombay Slide 82a GNR607 Lecture 32-34 B. Krishna Mohan

Band Multiplication

Multiply pixel by pixel input image and mask Image Black=0, Colored portion is original pixel values in input image

IIT Bombay Slide 82b GNR607 Lecture 32-34 B. Krishna Mohan

Band Addition

• Similar to Band Multiplication, band addition has

no direct practical application in adding gray levels of two bands of an image

• This method too can be used to mask a portion
• f the image and retain the remaining part.
• In the previous mask, make background 255,

desired portion 0, add pixel by pixel, truncate values above 255 to 255; result is desired portion of image within white background

IIT Bombay Slide 83 GNR607 Lecture 32-34 B. Krishna Mohan

Specialized Indices

• Combination of band differences, ratios

and additions can result in useful outputs that can highlight features like green vegetation

• One such feature is Normalized Difference

Vegetation Index (NDVI)

• NDVI(m,n) =

IIT Bombay Slide 84 GNR607 Lecture 32-34 B. Krishna Mohan

( , ) ( , ) ( , ) ( , )

IR R IR R

Band m n Band m n Band m n Band m n − +

NDVI

• NDVI results in high values where IR dominates

red wavelength. This happens where vegetation is present

• Range of NDVI is [-1 +1]
• NDVI has been widely used in a wide ranging of

agricultural, forestry and biomass estimation applications

• It is also used to measure the length of crop

growth and dry-down periods by comparing NDVI computed from multidate images

IIT Bombay Slide 85 GNR607 Lecture 32-34 B. Krishna Mohan

Input Image

IIT Bombay Slide 86 GNR607 Lecture 32-34 B. Krishna Mohan

NIR

IIT Bombay Slide 87 GNR607 Lecture 32-34 B. Krishna Mohan

RED

IIT Bombay Slide 88 GNR607 Lecture 32-34 B. Krishna Mohan

NDVI

IIT Bombay Slide 89 GNR607 Lecture 32-34 B. Krishna Mohan

Other Vegetation Indices

• Simple Ratio = NIR/RED
• NDVI6 = (Band 6 – Band 5)/(Band 6 + Band 5)
• NDVI7 = (Band 7 – Band 5)/(Band 7 + Band 5)
• Standard NDVITM = (TM4 – TM3)/(TM4 + TM3)

These are applicable when seven band data like Landsat Thematic Mapper data are available For IRS LISS3 imagery, NDVIIRS = IIT Bombay Slide 90 GNR607 Lecture 32-34 B. Krishna Mohan

4 3 4 3

( , ) ( , ) ( , ) ( , ) Band m n Band m n Band m n Band m n − +

IRS L4- NDVI

IIT Bombay Slide 91 GNR607 Lecture 32-34 B. Krishna Mohan

Fast Computation of NDVI

• Range of NDVI [-1, +1]
• Scale suitably to generate an NDVI image
• For example, NDVIscaled =127(1+NDVI)
• This ensures that the resultant NDVI has a

range of [0 254]

IIT Bombay Slide 92 GNR607 Lecture 32-34 B. Krishna Mohan

Selected Reflectance Curves

IIT Bombay Slide 93 GNR607 Lecture 32-34 B. Krishna Mohan From J.R. Jensen’s lecture notes at Univ. South Carolina

Used with permission

Time Series of 1984 and 1988 NDVI Measurements Derived from AVHRR Global Area Coverage (GAC) Data Region around El Obeid, Sudan, in Sub-Saharan Africa IIT Bombay Slide 94 GNR607 Lecture 32-34 B. Krishna Mohan From J.R. Jensen’s lecture notes at Univ. South Carolina

Used with permission

Simple Ratio v/s NDVI

IIT Bombay Slide 95 GNR607 Lecture 32-34 B. Krishna Mohan From J.R. Jensen’s lecture notes at Univ. South Carolina

Used with permission

Infrared Index

• Traditional NDVI does not work very well when

the soil is moist, as in case of wetlands. The Infrared Index (II) can tackle this situation better

• Several bands needed in the infrared region, as

in case of Landsat TM

IIT Bombay Slide 96 GNR607 Lecture 32-34 B. Krishna Mohan

4 5 4 5 TM TM TM TM

NIR MIR II NIR MIR − = +

Soil Line

IIT Bombay Slide 97 GNR607 Lecture 32-34 B. Krishna Mohan From J.R. Jensen’s lecture notes at Univ. South Carolina

Used with permission

Perpendicular Vegetation Index

PVI is defined as

IIT Bombay Slide 98 GNR607 Lecture 32-34 B. Krishna Mohan

( ) ( )

2 2 , , , , S R V R S NIR V NIR

PVI ρ ρ ρ ρ = − + −

A vegetation index that assumes that the reflectance in the NIR and red varies with increasing vegetation density (such as leaf area index) and that these variations are parallel to the soil baseline. Therefore, the perpendicular distance from the baseline in a NIR-red plot determines the vegetation density. See http://www.ccrs.nrcan.gc.ca/glossary/index_e.php?id=2179 for more definitions of various indices in remote sensing including PVI

Soil Adjusted Vegetation Index

The soil adjusted vegetation index (SAVI) introduces a soil calibration factor, L, to the NDVI equation to minimize soil background influences resulting from soil-plant spectral interactions: IIT Bombay Slide 99 GNR607 Lecture 32-34 B. Krishna Mohan ( )

(1 ) L NIR red SAVI NIR red L + − = + +

Ref:A. R. Huete, “A soil-adjusted vegetation index (SAVI),” Rem.

• Sens. Env., vol. 25, pp. 295-309, 1988.

Atmospherically Adjusted Vegetation Index (ARVI)

• The atmospheric effects are accounted for in ARVI

IIT Bombay Slide 100 GNR607 Lecture 32-34 B. Krishna Mohan

* * * * p nir p rb ARVI p nir p rb − = +

( )

* * * * p rb p red p blue p red

γ = − −

p* indicates the atmospherically corrected versions of NIR, Red and Blue bands for molecular scattering and

• zone absorption (p* may not be taken as multiplicative

factor) (Ref. J.R. Jensen’s notes)

Enhanced Vegetation Index

EVI is a mixture of SAVI and ARVI, in that both atmospheric effects and soil effects are accounted for.

IIT Bombay Slide 101 GNR607 Lecture 32-34 B. Krishna Mohan

1 2

* * * * * p nir p red EVI p nir C p red C p blue L − = + − +

C1 C1 and and C2 C2 describe the use of the blue band in correction of the red band describe the use of the blue band in correction of the red band for atmospheric aerosol scattering. The coefficients, for atmospheric aerosol scattering. The coefficients, C1 C1, , C2 C2, and , and L L are are empirically determined as 6.0, 7.5, and 1.0, respectively for MODIS. This empirically determined as 6.0, 7.5, and 1.0, respectively for MODIS. This algorithm has improved sensitivity to high biomass regions and improved algorithm has improved sensitivity to high biomass regions and improved vegetation monitoring through a de-coupling of the canopy background vegetation monitoring through a de-coupling of the canopy background signal and a reduction in atmospheric influences signal and a reduction in atmospheric influences

Source: Yoram J. Kaufman and Didier Tanre, Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS, IEEE Trans. GERS, vol. 30, no. 2, pp. 261-270, 1992

IIT Bombay Slide 102 GNR607 Lecture 32-34 B. Krishna Mohan

From J.R. Jensen’s lecture notes at Univ. South Carolina; used with permission

Normalized Difference Water Index

• normalized difference water index (NDWI),

defined as

• NDWI = (rgreen - rNIR)/(rgreen + rNIR)
• where rgreen and rNIR are the reflectance of green

and NIR bands, respectively

• NDWI also varies in the range -1 to +1

IIT Bombay Slide 101 GNR607 Lecture 32-34 B. Krishna Mohan

Source: Lei et al., PERS 2009, pp. 1307-1317

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