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FUNDAMENTALS OF DIGITAL IMAGESLecture 2
1GNR401 Dr. A. Bhattacharya
Image Data Structures
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Common Data Structures to Store Multiband Data
BIL – band interleaved by line
BSQ – band sequential
BIP – band interleaved by pixel
ExampleExample
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Band 1 Band 2 Band 3
1 1 1
111
11 1
3 3 3
333
33 3
2 2 2
222
22 2
3 bands , 9 pixels each in (3x3 format)
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Band interleaved by line storage format MxN Image; K Bands; One row on ground
B11 B12 … B1N
B21 B22 … B2N
…Bk1 Bk2 … BkN
A single file on disk or CD contains M.K rows, each having N columns; Every K rows in the file correspond to ONE ROWON THE GROUND
BIL
BIL
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BIL FILE STRUCTURE
Band 1 Row1
…
Band K Row1
Band1 Row2
…
Band K Row2
…
Band 1 Row M
…
Band K Row M
Image Size
M rows
N columns
K Bands
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Line #1, band #1 is stored first
Followed by line #1, band #2
Bands are inter-leaved by line
BIL format
1 1 1
111
11 1
3 3 3
333
33 3
2 2 2
222
22 2
111222333111222333111222333
BIL
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BIL is a popular format for storing multispectral images, and supported by most remote sensing software (ERDAS, PCI, …)
Well suited when multiband data analysis is required
Lot of data I/O involved when access to a single band image is needed on sequential access systems. Moderate overhead on random access systems
BSQ
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Band sequential method involves storing one full single band image after another
B11 B12 … B1N
B21 B22 … B2N
…BM1 BM2 … BMN
The image for the second band, …, up to Band K follow
BSQ
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Band 1 Row 1…Band 1 Row MBand2 Row 1…Band 2 Row M…Band K Row 1…Band K Row M
Band 1
Band 2
Band K
Image Size
M rows
N columns
K Bands
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Band #1 is stored first Followed by #2, #3 Bands are stored
sequentially
Band sequential (BSQ) format
1 1 1
111
11 1
3 3 3
333
33 3
2 2 2
222
22 2
111111111222222222333333333
BSQ
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Ideally suited when the multiband image is processed one band at a time, such as image enhancement, neighbourhood filtering, etc.
More overheads when all band values are required at each pixel
BIP
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Band interleaved by pixel Commonly used for storing color images, with red, green
and blue values alternating R G B R G B R G B …
Not used in present times to store satellite images Used in the early stages of Landsat data distribution
BIP
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Band 1 Band 2 … Band K Band 1 Band 2 … Band K … Band K
Row 1 Row 1 Row 1 Row 1 Row 1 Row 1 Row 1
Pixel 1 Pixel 1 … Pixel 1 Pixel 2 Pixel 2 Pixel 2 Pixel N
First Row
Band 1 Band 2 … Band K Band 1 Band 2 … Band K … Band K
Row 2 Row 2 Row 2 Row 2 Row 2 Row 2 Row 2
Pixel 1 Pixel 1 … Pixel 1 Pixel 2 Pixel 2 Pixel 2 Pixel N
Second Row
…
Band 1 Band 2 … Band K Band 1 Band 2 … Band K … Band K
Row M Row M Row M Row M Row M Row M Row M
Pixel 1 Pixel 1 … Pixel 1 Pixel 2 Pixel 2 Pixel 2 Pixel N
Mth Row
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1 1 1
111
11 1
3 3 3
333
33 3
2 2 2
222
22 2
123123123123123123123123123
Disk File Size of the image
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Rows x Cols x Bands x Bytes per pixel For the SPOT window,
500 x 500 x 3 x 1 = 750000 bytes ~ 750 KB In case of Ikonos image, storage is 2 bytes per pixel, 4
metres resolution, 4 bands 10 km x 10 km Ikonos multispectral image size on disk =
10000/4 x 10000/4 x 4 x 2 = 10000 x 5000 bytes ~ 50 MB Size of panchromatic image =
10000 x 10000 x 2 = 10000 x 20000 bytes ~200 MB
NOTE THE DIFFERENCE IN SIZE OF DATA!
Spectral bands and Spatial Resolution
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Spatial resolution is highest for panchromatic images
Lower for multispectral images
Reason?
In case of multispectral sensors, received energy is divided into band-wise slices; hence lesser amount of energy to detectors
Compensated by increasing time of observing ground features – hence lower spatial resolution
Image Sensing and Acquisition 17
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Image Formation Model
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Image Sampling & Quantization
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Image Sampling & Quantization
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Image Sampling & Quantization
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Sampling: Digitizing the coordinate values (spatial resolution)
Quantization: Digitizing the amplitude values (intensity levels)
Image Quantization
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Image Sampling
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Image Sampling
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Image Sampling
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Image Sampling
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Image Sampling 1
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Original 256 x256 128 x 128
Image Sampling 2
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Original 256 x256 64 x 64
Image Sampling 3
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Original 256 x256 32 x 32
Digital Image Representation
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Downsampling
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Downsampling
Re-Sampling
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Grey Level Quantization
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Grey Level Quantization 1
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Original 256 64
Grey Level Quantization 2
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Original 256 16
Grey Level Quantization 3
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Original 256 4
Grey Level Quantization 3
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Original 256 2
Digital Image Representation
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Digital Image Representation
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Basic relationships between pixels
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Basic relationships between pixels
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Basic relationships between pixels
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Basic relationships between pixels
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Basic relationships between pixels
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Set Logic Operations
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Distance Function
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Distance Function
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Distance Function
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Distance Function Examples
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