Integration of sensors for photogrammetry and remote sensing 8 th semester, MS 2005.

Slides:



Advertisements
Similar presentations
Digital Image Processing
Advertisements

Geometry of Aerial Photographs
REQUIRING A SPATIAL REFERENCE THE: NEED FOR RECTIFICATION.
Resurs-P. Capabilities. Standard products. A. Peshkun The 14 th International Scientific and Technical Conference “From imagery to map: digital photogrammetric.
© Spot Image Spot Image data and products Spot5 & Kompsat2 : complementary data for mapping large areas at large scales Michaël TONON Market Manager.
Aerial Photography Aerial platforms are primarily stable wing aircraft. Aircraft are often used to collect very detailed images and facilitate the collection.
September 17-20, 2007, Nessebar, Bulgaria
1:14 PM  Involves the manipulation and interpretation of digital images with the aid of a computer.  Includes:  Image preprocessing (rectification and.
Resolution Resolving power Measuring of the ability of a sensor to distinguish between signals that are spatially near or spectrally similar.
Some Basic Concepts of Remote Sensing
VIIth International Scientific and Technical Conference From Imagery to Map: Digital Photogrammetric Technologies ADS40 imagery processing using PHOTOMOD:
Image Preprocessing Image Preprocessing.
Radiometric and Geometric Errors
Remote Sensing II Sensors Konari, Iran Image taken 2/2/2000
Orbits and Sensors Multispectral Sensors
Line scanners Chapter 6. Frame capture systems collect an image of a scene of one instant in time The scanner records a narrow swath perpendicular to.
Satellite orbits.
Lecture 6 Multispectral Remote Sensing Systems. Overview Overview.
Remote sensing in meteorology
P.1 JAMES S. Bethel Wonjo Jung Geomatics Engineering School of Civil Engineering Purdue University APR Sensor Modeling and Triangulation for an.
Integration of sensors for photogrammetry and remote sensing 8 th semester, MS 2005.
Satellite Orbits Satellite Meteorology/Climatology Professor Menglin Jin.
Integration of sensors for photogrammetry and remote sensing 8 th semester, MS 2005.
9. GIS Data Collection.
Aerial photography and satellite imagery as data input GEOG 4103, Feb 20th Adina Racoviteanu.
Carolyn J. Merry NCRST-Flows The Ohio State University.
Lecture 21: Major Types of Satellite Imagery By Austin Troy University of Vermont Using GIS-- Introduction to GIS.
Satellite Imagery Data Products & Services November 2011Pacific Geomatics Ltd.1 Farida Raghina Manager, Sales & Customer Relations November 29, 2011.
1 Image Pre-Processing. 2 Digital Image Processing The process of extracting information from digital images obtained from satellites Information regarding.
HJ-1A/B CCD IMAGERY Geometric Distortions and Precise Geometric Correction Accuracy Analysis Changmiao Hu, Ping Tang
Ben Gurion University Mission scientists (PI's) : Gérard Dedieu & Arnon Kanieli G. Dedieu 1, O. Hagolle 2, A. Karnieli 3, S. Cherchali 2 P. Ferrier 2 and.
Image Formation. Input - Digital Images Intensity Images – encoding of light intensity Range Images – encoding of shape and distance They are both a 2-D.
WASET Defence, Computer Vision Theory and Application, Venice 13 th August – 14 th August 2015 A Four-Step Ortho-Rectification Procedure for Geo- Referencing.
Universität Hannover Institut für Photogrammetrie und GeoInformation Issues and Method for In-Flight and On-Orbit Calibration (only geometry) Karsten Jacobsen.
Orthorectification using
CE497 Urban Remote Sensing, Jie Shan1 Geometric rectification Homework 5.
Introduction to the Principles of Aerial Photography
7 elements of remote sensing process 1.Energy Source (A) 2.Radiation & Atmosphere (B) 3.Interaction with Targets (C) 4.Recording of Energy by Sensor (D)
Lecture 3 The Digital Image – Part I - Single Channel Data 12 September
Remote Sensing Data Acquisition. 1. Major Remote Sensing Systems.
February 3 Interpretation of Digital Data Bit and Byte ASCII Binary Image Recording Media and Formats Geometric corrections Image registration Projections.
Remote Sensing Remote Sensing is defined as the science and technology by which the characteristics of objects of interest can be identified, measured.
Chapter 8 Remote Sensing & GIS Integration. Basics EM spectrum: fig p. 268 reflected emitted detection film sensor atmospheric attenuation.
Digital Image Processing Definition: Computer-based manipulation and interpretation of digital images.
Test Ranges for Metric Calibration and Validation of Satellite Imaging Systems  Gene Dial  Jacek Grodecki
Introduction to Soft Copy Photogrammetry
Remote Sensing SPOT and Other Moderate Resolution Satellite Systems
CHARACTERISTICS OF OPTICAL SENSORS Course: Introduction to RS & DIP Mirza Muhammad Waqar Contact: EXT:2257 RG610.
CREATION OF DIGITAL SURFACE MODELS USING RESURS-P STEREO PAIRS Alexey Peshkun Deputy Head of Department 15th International Scientific and Technical Conference.
Lecture 6 Multispectral Remote Sensing Systems. Overview Overview.
High resolution sensors: Orbview Sensor Pan 1m: 0.45 – 0.90 (= 450 – 900nm) MS 4m: B: G: R: NIR: Data: 11.
Geosynchronous Orbit A satellite in geosynchronous orbit circles the earth once each day. The time it takes for a satellite to orbit the earth is called.
Mirza Muhammad Waqar GEOREFERENCING OF IMAGES BY EXPLOITING GEOMETRIC DISTORTIONS IN STEREO IMAGES OF UK DMC 1 Final Defense.
# x pixels Geometry # Detector elements Detector Element Sizes Array Size Detector Element Sizes # Detector elements Pictorial diagram showing detector.
Orbits and Sensors Multispectral Sensors. Satellite Orbits Orbital parameters can be tuned to produce particular, useful orbits Geostationary Sun synchronous.
Precise Georeferencing of Long Strips of Quickbird Imagery Dr Mehdi Ravanbakhsh Dr Clive Fraser WALIS Forum.
Geometric Correction of Remote Sensor Data
Geometric Preprocessing
Professor Ke-Sheng Cheng
Automatically Collect Ground Control Points from Online Aerial Maps
Basic Concepts of Remote Sensing
Satellite Photogrammetry
GOES-16 ABI Lunar Data Preparation to GIRO
MODIS Lunar Calibration Data Preparation and Results for GIRO Testing
Data Preparation for ASTER
Satellite Sensors – Historical Perspectives
Presentation Overview
Remote sensing in meteorology
2011 International Geoscience & Remote Sensing Symposium
DIGITAL PHOTOGRAMMETRY
Presentation transcript:

Integration of sensors for photogrammetry and remote sensing 8 th semester, MS 2005

Image geometry, georeferencing, orthorectification Specifications of VHR sensors for RS Camera models for IKONOS Specifications for IKONOS products Task: georeferencing of a Radar image

Overview on operational satellite sensors Resolution Swath (nadir) QB PAN 0.61m MS 2.44 m 16.5 km IKONOS PAN 1m MS 4 m 13.5 km SPOT 5 PAN + MS 2.5 m, 5m MS 10 m 60 km 120 km LANDSAT 7 PAN 15 m MS 30 m 185 km

VHR sensors for RS Specifications Orbital geometry Sensor geometry Image acquisition (‘agility’) Exterior orientation Interior orientation Spectral resolution Radiometric resolution Spatial resolution Temporal resolution

Orbit geometry Determines: how much of Earth surface can be covered how often the satellite revisit the same location Sun-synchronous orbits Altitude Orbit inclination

Sensor geometry Frame photogrammetric cameras, central projection Point across-track scanner Line along-track scanner Panoramic

Across-track scanner (whiskbroom) A.Oscillating mirror B.Detectors C.Instantaneous field of view (IFOV) D.Ground sampled distance (GSD) E.Angular field of view F.Swath Geometry not as stable as for line sensors Distortions due to a scanner mirror rotation Variations of the resolution cell size One-dimensional relief displacement Flight parameter distortions (roll, pitch, ‘crab’) Application: thermal scanners

Along-track scanner (pushbroom) A.Linear array of detectors B.Focal plane of the image C.Lens D.GSD Geometric integrity of a linear array of detectors Parameters of EO for each line Perspective along the scan line, orthographic in the direction of flight Calibration for each detector Limitation in spectral sensitivity

Image acquisition geometry Viewing directionNadir angle Stereo views cross-track along-track nadir forward backward nadir position off-nadir

IKONOS - image acquisition geometry

IKONOS – stereo image collection

Exterior orientation = satellite ephemeris and attitude Example: IKONOS Determination of ephemeris -post-processing of on-board GPS data Determination of attitude -measuring of star trackers + gyroscopes Important: relation between the satellite attitude co-ordinate system and the sensor co-ordinate system (pre-launched measurements + in-flight calibration)

Interior orientation Layout of the detector array, usually placed in the focal plane Optical distortion parameters Example : IKONOS Field Angle Map -allows to determine the line of sight vector for each pixel in the sensor co-ordinate system

Spectral resolution

Spatial resolution Nadir angle0°0°10°20°30°40°50° Pan across view direction Pan along view direction MS across view direction MS along view direction IKONOS: ground sampled distance [m] depending upon view direction

Radiometric resolution = number of bits used for a multispectral band  IKONOS: 11-bit Temporal resolution IKONOS:  = 40 , GSD = 1 m, revisit time 2.9 days

IKONOS camera model defines relation between object and image co- ordinates based on interior and exterior orientation parameters of the sensor specified by a provider of the sensor/system (Spaceimaging) complex, difficult to implement for a user simplification by ‘replacement camera models’ Rational Polynomial Camera (RPC) model Direct linear transform (DLT) model Affine model

Physical camera model Position of projective centre (PC) and attitude angles change from scan line to scan line

Rational Polynomial Camera (RPC) model relation between object co-ordinates (φ,λ,h) and image co-ordinates (r,c) given by rational polynomial functions: r’ = f r (φ’,λ’,h’)/g r (φ’,λ’,h’) c’ = f c (φ’,λ’,h’)/g c (φ’,λ’,h’) x’ … normalised co-ordinates x’ = (x-x_offset)/x_scale

Rational polynomial functions usually, 3 rd order polynomials are used f r = a 1 +a 2 φ’+a 3 λ’+a 4 h’ +a 5 φ’λ’+a 6 λ’h’+a 7 φ’h’+a 8 λ’ 2 +a 9 φ’ 2 + +a 10 h’ 2 +a 11 φ’λ’ h’+a 12 λ’ 3 +a 13 φ’ 2 λ’+a 14 λ’h’ 2 +a 15 φ’λ’ 2 + +a 16 φ’ 3 +a 17 φ’ h’ 2 +a 18 λ’ 2 h’+a 19 φ’ 2 λ’+a 20 h’ 3 g r = b 1 +b 2 φ’+b 3 λ’+b 4 h’ +b 5 φ’λ’+b 6 λ’h’+b 7 φ’h’+b 8 λ’ 2 +b 9 φ’ 2 + +b 10 h’ 2 +b 11 φ’λ’ h’+b 12 λ’ 3 +b 13 φ’ 2 λ’+b 14 λ’h’ 2 +b 15 φ’λ’ 2 + +b 16 φ’ 3 +b 17 φ’ h’ 2 +b 18 λ’ 2 h’+b 19 φ’ 2 λ’+b 20 h’ 3 similarly, RPC coefficients c 1, …, c 20, d 1, …, d 20 in functions f c and g c

RPC coefficients calculated in least-squares adjustment from 3D grid points co-ordinates of 3D grid points generated using a physical camera model comparison of RPC and a physical camera model max. error using independent check points 0.04 pel RMS error using independent check points 0.01 pel (Grodecky, J., Dial, G., IKONOS geometric accuracy, Space Imaging, 2001)

3D grid for derivation RPC coefficients

Refinement of RPC model improving absolute positional accuracy of the georeferenced image by adding GCPs finding parameters of affine transformation r corr =a 1 +a 2 r’+a 3 c’ c corr =b 1 +b 2 r’+b 3 c’ improvement of accuracy from several m up to 0.5m if accurate and well distributed GCPs are available

Other replacement camera models Direct linear transform model derived from collinearity equation (projective geometry) x=(a 1 +a 2 X+a 3 Y+a 4 Z)/(c 1 +c 2 X+c 3 Y+c 4 Z) y=(b 1 +b 2 X+b 3 Y+b 4 Z)/(c 1 +c 2 X+c 3 Y+c 4 Z) Affine model x=a 1 +a 2 X+a 3 Y y=b 1 +b 2 X+b 3 Y

Product Horizontal accuracy [m] Ortho- rectification GCPDEM Geo15--- Reference25+-±22m Pro10+-±10m Precision4++±10m Precision Plus2++±3m IKONOS products IKONOS product guide

Links Articles: –Grodecki, J., Dial, G. (2001).: IKONOS Geometric Accuracy. Proceedings of Joint Workshop of ISPRS Working Groups I/2, I/5 and IV/7 on High Resolution Mapping from Space 2001, University of Hanover, Hanover, Germany, Sept , 2001IKONOS Geometric Accuracy. Products description –Space Imaging (IKONOS)Space Imaging –DigitalGlobe (QuickBird)DigitalGlobe