FUNDAMENTALS OF DIGITAL IMAGES

Similar documents
Image interpretation I and II

Files Used in This Tutorial. Background. Calibrating Images Tutorial

Sommersemester Prof. Dr. Christoph Kleinn Institut für Waldinventur und Waldwachstum Arbeitsbereich Fernerkundung und Waldinventur.

Remote Sensing and Image Processing: 4

Spectral Signatures. Vegetation. 40 Soil. Water WAVELENGTH (microns)

Introduction to Remote Sensing

Image interpretation. Aliens create Indian Head with an ipod? Badlands Guardian (CBC) This feature can be found 300 KMs SE of Calgary.

Image Processing (EA C443)

Image and Multidimensional Signal Processing

Lecture 2. Electromagnetic radiation principles. Units, image resolutions.

EO Data Today and Application Fields. Denise Petala

Lab 1 Introduction to ENVI

Introduction to Remote Sensing

Remote sensing in archaeology from optical to lidar. Krištof Oštir ModeLTER Scientific Research Centre of the Slovenian Academy of Sciences and Arts

Satellite Imagery and Remote Sensing. DeeDee Whitaker SW Guilford High EES & Chemistry

GeoBase Raw Imagery Data Product Specifications. Edition

CHARACTERISTICS OF REMOTELY SENSED IMAGERY. Radiometric Resolution

Digital Imaging Rochester Institute of Technology

Aral Sea profile Selection of area 24 February April May 1998

Satellite/Aircraft Imaging Systems Imaging Sensors Standard scanner designs Image data formats

Remote Sensing Platforms

An Introduction to Geomatics. Prepared by: Dr. Maher A. El-Hallaq خاص بطلبة مساق مقدمة في علم. Associate Professor of Surveying IUG

Geo/SAT 2 INTRODUCTION TO REMOTE SENSING

MULTISPECTRAL IMAGE PROCESSING I

Overview of how remote sensing is used by the wildland fire community.

v References Nexus RS Workshop (English Version) August 2018 page 1 of 44

REMOTE SENSING DATA PRODUCTS AND FORMATS

Remote Sensing. The following figure is grey scale display of SPOT Panchromatic without stretching.

Some Basic Concepts of Remote Sensing. Lecture 2 August 31, 2005

Remote Sensing Platforms

Lecture 6: Multispectral Earth Resource Satellites. The University at Albany Fall 2018 Geography and Planning

Urban Classification of Metro Manila for Seismic Risk Assessment using Satellite Images

(Refer Slide Time: 1:28)

REMOTE SENSING. Topic 10 Fundamentals of Digital Multispectral Remote Sensing MULTISPECTRAL SCANNERS MULTISPECTRAL SCANNERS

Important Missions. weather forecasting and monitoring communication navigation military earth resource observation LANDSAT SEASAT SPOT IRS

Final Examination Introduction to Remote Sensing. Time: 1.5 hrs Max. Marks: 50. Section-I (50 x 1 = 50 Marks)

Int n r t o r d o u d c u ti t on o n to t o Remote Sensing

Introduction to Remote Sensing Part 1

ECE/OPTI533 Digital Image Processing class notes 288 Dr. Robert A. Schowengerdt 2003

1 W. Philpot, Cornell University The Digital Image

CHARACTERISTICS OF REMOTELY SENSED IMAGERY. Spatial Resolution

Basic Digital Image Processing. The Structure of Digital Images. An Overview of Image Processing. Image Restoration: Line Drop-outs

BV NNET User manual. V0.2 (Draft) Rémi Lecerf, Marie Weiss

Image transformations

Real Time Visualization of Full Resolution Data of Indian Remote Sensing Satellite

Evaluation of FLAASH atmospheric correction. Note. Note no SAMBA/10/12. Authors. Øystein Rudjord and Øivind Due Trier

Module 3 Introduction to GIS. Lecture 8 GIS data acquisition

Classification in Image processing: A Survey

NON-PHOTOGRAPHIC SYSTEMS: Multispectral Scanners Medium and coarse resolution sensor comparisons: Landsat, SPOT, AVHRR and MODIS

29 th Annual Louisiana RS/GIS Workshop April 23, 2013 Cajundome Convention Center Lafayette, Louisiana

8. EDITING AND VIEWING COORDINATES, CREATING SCATTERGRAMS AND PRINCIPAL COMPONENTS ANALYSIS

EVALUATION OF SATELLITE IMAGE FUSION USING WAVELET TRANSFORM

366 Glossary. Popular method for scale drawings in a computer similar to GIS but without the necessity for spatial referencing CEP

1. Theory of remote sensing and spectrum

What is Remote Sensing? Contents. Image Fusion in Remote Sensing. 1. Optical imagery in remote sensing. Electromagnetic Spectrum

ROLE OF SATELLITE DATA APPLICATION IN CADASTRAL MAP AND DIGITIZATION OF LAND RECORDS DR.T. RAVISANKAR GROUP HEAD (LRUMG) RSAA/NRSC/ISRO /DOS HYDERABAD

9/12/2011. Training Course Remote Sensing Basic Theory & Image Processing Methods September 2011

Ge111A Remote Sensing and GIS Lecture

Lecture 2: Digital Image Fundamentals -- Sampling & Quantization

Single Error Correcting Codes (SECC) 6.02 Spring 2011 Lecture #9. Checking the parity. Using the Syndrome to Correct Errors

COMPARISON OF INFORMATION CONTENTS OF HIGH RESOLUTION SPACE IMAGES

CS 548: Computer Vision REVIEW: Digital Image Basics. Spring 2016 Dr. Michael J. Reale

RGB colours: Display onscreen = RGB

GEOMETRIC RECTIFICATION OF EUROPEAN HISTORICAL ARCHIVES OF LANDSAT 1-3 MSS IMAGERY

Application of Satellite Image Processing to Earth Resistivity Map

VICARIOUS CALIBRATION SITE SELECTION FOR RAZAKSAT MEDIUM-SIZED APERTURE CAMERA (MAC)

Digital Image Processing

Data Acquisition & Computer Control

Digitization and fundamental techniques

Digital Image Fundamentals. Digital Image Processing. Human Visual System. Contents. Structure Of The Human Eye (cont.) Structure Of The Human Eye

Abstract Quickbird Vs Aerial photos in identifying man-made objects

Digital Image Fundamentals. Digital Image Processing. Human Visual System. Contents. Structure Of The Human Eye (cont.) Structure Of The Human Eye

Digital Image Processing

Module 11 Digital image processing

High Resolution Satellite Data for Forest Management. - Algorithm for Tree Counting -

RADIOMETRIC CALIBRATION

Lecture 13: Remotely Sensed Geospatial Data

EXAMPLES OF TOPOGRAPHIC MAPS PRODUCED FROM SPACE AND ACHIEVED ACCURACY CARAVAN Workshop on Mapping from Space, Phnom Penh, June 2000

Introduction. Introduction. Introduction. Introduction. Introduction

December 28, Dr. Praveen Sankaran (Department of ECE NIT Calicut DIP)

Land Cover Analysis to Determine Areas of Clear-cut and Forest Cover in Olney, Montana. Geob 373 Remote Sensing. Dr Andreas Varhola, Kathry De Rego

IKONOS High Resolution Multispectral Scanner Sensor Characteristics

9/12/2011. Training Course Remote Sensing Basic Theory & Image Processing Methods September 2011

Removing Thick Clouds in Landsat Images

Sources of Geographic Information

United States Patent (19) Laben et al.

Remote Sensing of the Environment An Earth Resource Perspective John R. Jensen Second Edition

REMOTE SENSING INTERPRETATION

Using Freely Available. Remote Sensing to Create a More Powerful GIS

DIGITAL IMAGE PROCESSING (COM-3371) Week 2 - January 14, 2002

CHAPTER 7: Multispectral Remote Sensing

Byte = More common: 8 bits = 1 byte Abbreviation:

US Commercial Imaging Satellites

Lecture 3 Digital image processing.

APCAS/10/21 April 2010 ASIA AND PACIFIC COMMISSION ON AGRICULTURAL STATISTICS TWENTY-THIRD SESSION. Siem Reap, Cambodia, April 2010

MODULE 4 LECTURE NOTES 4 DENSITY SLICING, THRESHOLDING, IHS, TIME COMPOSITE AND SYNERGIC IMAGES

2013 LMIC Imaging Workshop. Sidney L. Shaw Technical Director. - Light and the Image - Detectors - Signal and Noise

Digital images. Digital Image Processing Fundamentals. Digital images. Varieties of digital images. Dr. Edmund Lam. ELEC4245: Digital Image Processing

STRIPING NOISE REMOVAL OF IMAGES ACQUIRED BY CBERS 2 CCD CAMERA SENSOR

Digital Photogrammetry. Presented by: Dr. Hamid Ebadi

Transcription:

FUNDAMENTALS OF DIGITAL IMAGES Lecture

Image Data Structures Common Data Structures to Store Multiband Data BIL band interleaved by line BSQ band sequential BIP band interleaved by pixel

Example Band Band Band bands, 9 pixels each in (x format)

BIL 4 Band interleaved by line storage format MxN Image; K Bands; One row on ground B B B N B B B N B k B k B kn 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

BIL 5 BIL FILE STRUCTURE Band Row Band K Row Band Row Band K Row Band Row M Band K Row M Image Size M rows N columns K Bands

6 Line #, band # is stored first Followed by line #, band # Bands are inter-leaved by line BIL format

BIL 7 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 8 Band sequential method involves storing one full single band image after another B B B N B B B N B M B M B MN The image for the second band,, up to Band K follow

BSQ 9 Image Size M rows N columns K Bands Band Row Band Row M Band Row Band Row M Band K Row Band K Row M Band Band Band K

0 Band # is stored first Followed by #, # Bands are stored sequentially Band sequential (BSQ) format

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

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

BIP First Row Band Band Band K Band Band Band K Band K Row Row Row Row Row Row Row Pixel Pixel Pixel Pixel Pixel Pixel Pixel N Band Band Band K Band Band Band K Band K Row Row Row Row Row Row Row Pixel Pixel Pixel Pixel Pixel Pixel Pixel N Second Row M th Row Band Band Band K Band Band Band K Band K Row M Row M Row M Row M Row M Row M Row M Pixel Pixel Pixel Pixel Pixel Pixel Pixel N

4

Disk File Size of the image 5 Rows x Cols x Bands x Bytes per pixel For the SPOT window, 500 x 500 x x = 750000 bytes ~ 750 KB In case of Ikonos image, storage is bytes per pixel, 4 metres resolution, 4 bands 0 km x 0 km Ikonos multispectral image size on disk = 0000/4 x 0000/4 x 4 x = 0000 x 5000 bytes ~ 50 MB Size of panchromatic image = 0000 x 0000 x = 0000 x 0000 bytes ~00 MB NOTE THE DIFFERENCE IN SIZE OF DATA!

Spectral bands and Spatial Resolution 6 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 7

Image Formation Model 8

Image Sampling & Quantization 9

Image Sampling & Quantization 0

Image Sampling & Quantization Sampling: Digitizing the coordinate values (spatial resolution) Quantization: Digitizing the amplitude values (intensity levels)

Image Quantization

Image Sampling

Image Sampling 4

Image Sampling 5

Image Sampling 6

Image Sampling 7 Original 56 x56 8 x 8

Image Sampling 8 Original 56 x56 64 x 64

Image Sampling 9 Original 56 x56 x

Digital Image Representation 0

Downsampling

Downsampling

Re-Sampling

Grey Level Quantization 4

Grey Level Quantization 5 Original 56 64

Grey Level Quantization 6 Original 56 6

Grey Level Quantization 7 Original 56 4

Grey Level Quantization 8 Original 56

Digital Image Representation 9

Digital Image Representation 40

Basic relationships between pixels 4

Basic relationships between pixels 4

Basic relationships between pixels 4

Basic relationships between pixels 44

Basic relationships between pixels 45

Set Logic Operations 46

Distance Function 47

Distance Function 48

Distance Function 49

Distance Function Examples 50