Contents of the Lecture Display of Remotely Sensed Images False - - PowerPoint PPT Presentation

Contents of the Lecture

Display of Remotely Sensed Images

• False color composites
• Natural color composites
• Gray scale images

Interleaving Formats for Multiband Images

• BIL
• BSQ
• BIP

Impact of Resolution on File Sizes

IIT Bombay Slide 1 GNR607 Lecture 05 B. Krishna Mohan July 31, 2014 Lecture 05 Image Display and Data Formats

Image Display

Red Gun Image Data

• n Disk

Blue Gun Green Gun

Image Display System

IIT Bombay Slide 2 GNR607 Lecture 05 B. Krishna Mohan

Concept of a Color Composite

• In order to generate a color display of a

satellite image on the monitor, we need to choose

– Data to represent in red color – Data to represent in green color – Data to represent in blue color

• Such a display is known as a color

composite

IIT Bombay Slide 3 GNR607 Lecture 05 B. Krishna Mohan

False Color Composite

• A False Color Composite (FCC) is formed

when the data assigned to red / green / blue color on the display is collected outside the visible region

• A standard FCC comprises:

Wavelength of Data Display – Near Infrared wavelength Red Color – Red wavelength Green Color – Green wavelength Blue color

IIT Bombay Slide 4 GNR607 Lecture 05 B. Krishna Mohan

Reflectance Spectra of Earth Objects

IIT Bombay Slide 4a GNR607 Lecture 05 B. Krishna Mohan

Example of FCC

IIT Bombay Slide 5 GNR607 Lecture 05 B. Krishna Mohan

IIT Bombay Slide 5a GNR607 Lecture 05 B. Krishna Mohan

Example of FCC

IIT Bombay Slide 5b GNR607 Lecture 05 B. Krishna Mohan 23.25m x 23.25m

Natural Color Composite

• A Natural Color Composite (NCC) is

formed when the data assigned to red/green/blue is collected in the same wavelengths

• For instance:

Wavelength of Data Display

– Red wavelength Red Color – Green wavelength Green Color – Blue wavelength Blue color

IIT Bombay Slide 6 GNR607 Lecture 05 B. Krishna Mohan

Natural Color Composite

IIT Bombay Slide 7 GNR607 Lecture 05 B. Krishna Mohan

IIT Bombay Slide 7a GNR607 Lecture 05 B. Krishna Mohan

IIT Bombay Slide 7b GNR607 Lecture 05 B. Krishna Mohan BHUVAN (2D) – PART OF MUMBAI

Black & White Image

• A black/white image is one that has no

color but only white, black and shades of gray.

• The smallest value at a pixel is 0 (black)
• The largest value is 2L-1 (white)
• Intermediate values represent shades of

gray, from black increasing towards white

• For L=8, black = 0, white = 255

IIT Bombay Slide 8 GNR607 Lecture 05 B. Krishna Mohan

Gray Scale

black dark gray light gray white IIT Bombay Slide 9 GNR607 Lecture 05 B. Krishna Mohan

Gray Scale Images

• Examples of gray scale images in R.S.

– An image of a single band of a multisp. image – An image from radar sensor (SAR image) – An image from panchromatic sensor

• How does this happen on a display

monitor?

When Red, Green and Blue display guns are fed the same signal, the resulting display on the screen will be black&white IIT Bombay Slide 10 GNR607 Lecture 05 B. Krishna Mohan

MUMBAI Data: IRS-1C, PAN Consists of 1024x1024 pixels.

Panchromatic Image

GNR607 Lecture 05 B. Krishna Mohan IIT Bombay Slide 11

Bangalore Data: SPOT, PLA Consists of 1024x1024 pixels.

Panchromatic Image

GNR607 Lecture 05 B. Krishna Mohan IIT Bombay Slide 11a

A selected range of gray levels is assigned an artificial color for highlighting the feature that is originally found in that range

Pseudocolor Image

GNR607 Lecture 05 B. Krishna Mohan IIT Bombay Slide 11b

A number of eddies visible in the Pacific Ocean in this pseudo-color scene. http://earthobservatory.nasa.gov/IOTD/view

Pseudocolor Image

GNR607 Lecture 05 B. Krishna Mohan IIT Bombay Slide 11b

http://911encyclopedia.com/wiki/index.php/AVIRI

Common Data Structures to Store Multiband Data

IIT Bombay Slide 12 GNR607 Lecture 05 B. Krishna Mohan

Common Data Structures to Store Multiband Data

• BIL – band interleaved by line
• BSQ – band sequential
• BIP – band interleaved by pixel

IIT Bombay Slide 13 GNR607 Lecture 05 B. Krishna Mohan

Image Acquisition

Band 1 Band 2 Band 3

…

Ground

Width equal to pixel width Direction of satellite motion IIT Bombay Slide 14 Optics GNR607 Lecture 05 B. Krishna Mohan

BIL

• 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 ROW ON THE GROUND IIT Bombay Slide 15 GNR607 Lecture 05 B. Krishna Mohan

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 IIT Bombay Slide 16 GNR607 Lecture 05 B. Krishna Mohan

BIL

• 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

IIT Bombay Slide 17 GNR607 Lecture 05 B. Krishna Mohan

BSQ

• Band sequential method involves storing
• ne 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

IIT Bombay Slide 18 GNR607 Lecture 05 B. Krishna Mohan

BSQ FILE STRUCTURE

Band 1 Row 1 … Band 1 Row M Band2 Row 1 … Band 2 Row M … Band K Row 1 … Band K Row M Image Size M rows N columns K Bands

Band 1 Band 2 Band K

IIT Bombay Slide 19 GNR607 Lecture 05 B. Krishna Mohan

BSQ

• 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

IIT Bombay Slide 20 GNR607 Lecture 05 B. Krishna Mohan

BIP

• 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

IIT Bombay Slide 21 GNR607 Lecture 05 B. Krishna Mohan

BIP Structure

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 IIT Bombay Slide 22 GNR607 Lecture 05 B. Krishna Mohan

Formats for Distributing Remotely Sensed Data

• Suppliers provide image data in different

formats:

– LGSOWG (super-structure format) – Fast format – HDF format – GeoTiff format – Proprietary software format (e.g., ERDAS IMG) IIT Bombay Slide 23 GNR607 Lecture 05 B. Krishna Mohan

Superstructure Format

IIT Bombay Slide 24 GNR607 Lecture 05 B. Krishna Mohan

• Very exhaustive data format
• Levels of processing
• Level 0 (Raw data)
• Level 1 (Radiometrically corrected)
• Level 2 (Radiometrically and geometrically corrected)
• Digital File Volume consists of five files
• Some differences for mag. tapes and CD-ROMs

Digital File Volume

IIT Bombay Slide 25 GNR607 Lecture 05 B. Krishna Mohan

• Volume Directory File (Volume descriptor, File

Pointers and text record)

• Leader File (Descriptor, Header, ancillary,

Calibration, histogram, map projection, GCP, annotation, Boundary, and Boundary annotation Record)

• Image Data File (BIL or BSQ)
• Trailer File (Description and trailer records)
• Null volume File

IIT Bombay Slide 26 GNR607 Lecture 05 B. Krishna Mohan CDINFO – a file describing the Satellite name, Product Code, Path, Row, Date Of Pass, Sensor, Volume number

• etc. It is basically gives the information about the contents
• f CD. Typical content of a CDINFO file for P6 will look like

this. Product1 – a folder containing files listed in the previous slide

NOTE: Data were distributed in the past on magentic tapes prior to CDs and DVDs. Even today 100 GB tapes are used for backups in data centres

CD Product Structure

IIT Bombay Slide 27 GNR607 Lecture 05 B. Krishna Mohan PRODUCT1- a directory containing following files. VOLUME.SensorCode/DAT – the Volume Directory File. LEADER.SensorCode/DAT - the Leader File. IMAGERYb.SensorCode/DAT- the Imagery File. TRAILER.SensorCode/DAT – the Trailer File. NULL.SensorCode/DAT - the Null Volume File. Where b=Band number. In case of PAN ‘b’ will not be

• present. and SensorCode=Three Character Sensor Code

File Details

IIT Bombay Slide 28 GNR607 Lecture 05 B. Krishna Mohan Options possible: One CD containing one full scene One scene stored onto two CDs Two scenes stored on one full CD All possibilities are accommodated by the superstructure format Most commercial software vendors read this format

CD Product Structure

IRS Sensors

IIT Bombay Slide 29 GNR607 Lecture 05 B. Krishna Mohan

IIT Bombay Slide 29a GNR607 Lecture 05 B. Krishna Mohan GeoTIFF format Supports additional tags to represent geographic information like datum, projection, lat-long coordinates etc. Supports storing multiband data (>3 bands) in a single file Open format (not proprietary)

Extension to TIFF

Typical Data of interest

IIT Bombay Slide 30 GNR607 Lecture 05 B. Krishna Mohan

• Image dimensions (in rows, cols)
• Latitude-longitude extents of scene
• Sun angle (to interpret shading differences, shadows

etc.)

• Number of bands
• Type of processing done on the raw data
• Full scene in one CD / multiple CDs / multiple scenes on

single CD

• Ground Control Points

Some Sample Calculations

Given pixel area on ground: l x b metre2, Size of image: M x N Area of image on ground: L x B km2 Number of bands: K Format of storage: BIL Extract from the image a window of Area L1 x B1 (L1 < L, B1 < B) OR Size M1 x N1 (M1 < M, N1 < N) IIT Bombay Slide 31 GNR607 Lecture 05 B. Krishna Mohan

Image Size and Ground Area

M Rows L kms N Columns; B kms One pixel; l metres x b metres GNR607 Lecture 05 B. Krishna Mohan IIT Bombay Slide 32

Area of Sub-image

• Number of rows in the sub-image = L1 / l
• Number of cols in the sub-image = B1/b
• What are the coordinates of the window?

B1 L1

GNR607 Lecture 05 B. Krishna Mohan IIT Bombay Slide 33

Sub-image Extraction

• The user must specify the location of the sub-

image in the overall image

• This may be done using interactive facility such

as given the left top coordinate, extract a sub- image of L1xB1 area, a window of M1 x N1 etc.

• Work out an algorithm to extract this assuming

BIL / BSQ / BIP organization of the data

IIT Bombay Slide 34 GNR607 Lecture 05 B. Krishna Mohan

Algorithm

• Open the input image for read and output image for write
• perations
• Skip the pixels up to the left top corner of the sub-image
• From the left-top corner, copy desired pixels into a

separate array

• Store this array in an output file
• Close the image files
• Remember – this should be done for K bands, stored

in BIL/BSQ/BIP form IIT Bombay Slide 35 GNR607 Lecture 05 B. Krishna Mohan

Example

• Given a SPOT-1 multispectral image

covering an area of size 25km x 25km, extract the middle 10km x 10km window

• Assume BSQ form of storage for input as

well as output

• SPOT-1 acquired the image in three

spectral bands

GNR607 Lecture 05 B. Krishna Mohan IIT Bombay Slide 36

Disk File Size of the image

• 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!

GNR607 Lecture 05 B. Krishna Mohan IIT Bombay Slide 37

Images to cover entire globe

• Surface area of Earth = 5.1 x 108 km2
• One Landsat scene covers 185 km x185 km
• If entire Earth is covered by Landsat images without
• verlap, number of scenes required =

5.1 x 108 / (185)2 = ~ 14902

• At 270MB/scene, ~3.84 TB
• If covered by images having sidelap and overlap,

more images are required GNR607 Lecture 05 B. Krishna Mohan IIT Bombay Slide 37

Mathematical Preliminaries

• Vectors and Matrices

– Vector spaces – Basis vectors – Orthogonal and orthonormal set of vectors – Linear independence

• Properties of Matrices

– Eigenvectors and eigenvalues – Operations on matrices

• Probability and random variables

– Axioms of probability – Independence and mutual exclusion – Random variable – Properties of random variables

GNR607 Lecture 05 B. Krishna Mohan IIT Bombay Slide 38

Mathematical Preliminaries

• Probability and random variables contd.

– Expectation – Multiple random variables – Cross correlation, covariance – Independence and uncorrelatedness – Information, mutual information, entropy

• Linear Systems

– Properties of linear systems – Impulse response – Shift invariance – Convolution – Relation to Fourier transform

GNR607 Lecture 05 B. Krishna Mohan IIT Bombay Slide 39

Contd…