1/19/2012 1 GNR630 Introduction to Geospatial Technologies Instructors: Prof. (Mrs.) P. Venkatachalam Prof. B. Krishna Mohan Prof. S.S. Gedam CSRE, IIT Bombay pvenk/bkmohan/[email protected]Slot 6 Lecture 5-6 Neighborhood Operations and Multiband Operations January 20/25, 2012 11.05AM – 12.30PM Contents of the Lecture • Concept of Neighborhood • Image Smoothing • Edge Enhancement • Color Transforms • Band Arithmetic IIT Bombay Slide 1 GNR630 Lecture 5-6 B. Krishna Mohan January 20/25, 2012 Lecture 5-6 Neighborhood Operations and Multiband Operations
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1/19/2012
1
GNR630 Introduction to Geospatial
TechnologiesInstructors: Prof. (Mrs.) P. Venkatachalam
• Advantage – in one pass image is generated in range 0-255
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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.
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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
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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 of the image and retain the remaining part.
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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) =
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( , ) ( , )
( , ) ( , )
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
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Inp
ut Im
ag
e
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NIR
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RED
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NDVI
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Other Vegetation Indices
• Simple Ratio = Red/NIR• 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 =
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4 3
4 3
( , ) ( , )
( , ) ( , )
Band m n Band m n
Band m n Band m n
−
+
IRS L4-NDVI
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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]
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Selected Reflectance Curves
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GNR630 Lecture 5-6 B. Krishna Mohan
From J.R.
Jensen’s
lecture notes
at Univ. South
Carolina
Used with permission
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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 90
GNR630 Lecture 5-6 B. Krishna Mohan
From J.R.
Jensen’s
lecture notes
at Univ. South
Carolina
Used with permission
Simple Ratio v/s NDVI
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GNR630 Lecture 5-6 B. Krishna Mohan
From J.R.
Jensen’s
lecture notes
at Univ. South
Carolina
Used with permission
1/19/2012
48
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
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Data Fusion
• Most commonly employed by endusers of
remotely sensed data
• Supported by most software packages
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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
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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)
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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)
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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
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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)
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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
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Image Sharpening
• MShr = f(MSlr, PANhr) , where
• MS = multispectral Image
• PAN = Panchromatic Image
• lr = low resolution
• hr = high resolution
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Sharpening Techniques
• Principal Component Analysis method
• Intensity-Hue-Saturation method
• Ratio-based (Brovey Transform)
• Arithmetic algorithm
• Multiplicative
• Wavelet Transform method
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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
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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.