ATI Courses Professional Development Short Course Remote Sensing Information Extraction

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This three-day workshop will review remote sensing concepts and vocabulary including resolution, sensing platforms, electromagnetic spectrum and energy flow profile. The workshop will provide an overview of the current and near-term status of operational platforms and sensor systems. The focus will be on methods to extract information from these data sources. The spaceborne systems include the following; 1) high spatial resolution (< 5m) systems, 2) medium spatial resolution (5-100m) multispectral, 3) low spatial resolution (>100m) multispectral, 4) radar, and 5) hyperspectral. The two directional relationships between remote sensing and GIS will be examined. Procedures for geometric registration and issues of cartographic generalization for creating GIS layers from remote sensing information will also be discussed.

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ATI Courses Professional Development Short Course Remote Sensing Information Extraction

  1. 1. Professional Development Short Course On: Remote Sensing Information Extraction Instructor: Dr. Barry Haack ATI Course Schedule: http://www.ATIcourses.com/schedule.htm ATI's Remote Sensing Information Extraction: http://www.aticourses.com/remote_sensing_info_extraction.htm
  2. 2. Remote Sensing Information Extraction March 16-18, 2010 Chantilly, Virginia $1490 (8:30am - 4:00pm) "Register 3 or More & Receive $10000 each Course Outline Off The Course Tuition." 1. Remote Sensing Introduction. Definitions, resolutions, active-passive. 2. Platforms. Airborne, spaceborne, advantages and limitations. 3. Energy Flow Profile. Energy sources, atmospheric interactions, reflectance curves, emittance. 4. Aerial Photography. Photogrammetric fundamentals of photo acquisition. 5. Film Types. Panchormatic, normal color, color Summary infrared, panchromatic infrared. This 3-day workshop will review remote sensing 6. Scale Determination. Point versus average concepts and vocabulary including resolution, sensing scale. Methods of determination of scale. platforms, electromagnetic spectrum and energy flow profile. The workshop will provide an overview of the 7. Area and Height Measurements. Tools and current and near-term status of operational platforms procedures including relative accuracies. and sensor systems. The focus will be on methods to 8. Feature Extraction. Tone, texture, shadow, extract information from these data sources. The size, shape, association. spaceborne systems include the following; 1) high 9. Land Use and Land Cover. Examples, spatial resolution (< 5m) systems, 2) medium spatial classification systems definitions, minimum resolution (5-100m) multispectral, 3) low spatial mapping units, cartographic generalization. resolution (>100m) multispectral, 4) radar, and 5) hyperspectral. 10. Source materials. Image processing The two directional relationships between remote software, organizations, literature, reference sensing and GIS will be examined. Procedures for materials. geometric registration and issues of cartographic 11. Spaceborne Remote Sensing. Basic generalization for creating GIS layers from remote terminology and orbit characteristics. Distinction sensing information will also be discussed. between research/experimental, national technical assets, and operational systems. Instructor 12. Multispectral Systems. Cameras, scanners Dr. Barry Haack is a Professor of Geographic and linear arrays, spectral matching. Cartographic Sciences at George Mason University. 13. Moderate Resolution MSS. Landsat, SPOT, He was a Research Engineer at ERIM and has held IRS, JERS. fellowships with NASA Goddard, the US Air Force and 14. Coarse Resolution MSS. Meteorological the Jet Propulsion Laboratory. His primary professional Systems, AVHRR, Vegetation Mapper. interest is basic and applied science using remote sensing and he has over 100 professional publications 15. High Spatial Resolution. IKONOS, and has been a recipient of a Leica-ERDAS award for EarthView, Orbview. a research manuscript in Photogrammetric Engineering 16. Radar. Basic concepts, RADARSAT, ALMAZ, and Remote Sensing. He has served as a consultant to SIR. the UN, FAO, World Bank, and various governmental 17. Hyperspectral. AVIRIS, MODIS, Hyperion. agencies in Africa, Asia and South America. He has provided workshops to USDA, US intelligence 18. GIS-Remote Sensing Integration. Two agencies, US Census, and ASPRS. Recently he was a directional relationships between remote sensing Visiting Fulbright Professor at the University of Dar es and GIS. Data structures. Salaam in Tanzania and has current projects in Nepal 19. Geometric Rectification. Procedures to with support from the National Geographic Society. rectify remote sensing imagery. 20. Digital Image Processing. Preprocessing, image enhancements, automated digital What You Will Learn classification. • Operational parameters of current sensors. 21. Accuracy Assessments. Contingency • Visual and digital information extraction procedures. matrix, Kappa coefficient, sample size and • Photogrammetric rectification procedures. selection. • Integration of GIS and remote sensing. 22. Multiscale techniques. Ratio estimators, • Accuracy assessments. double and nested sampling, area frame • Availability and costs of remote sensing data. procedures. 12 – Vol. 98 Register online at www.ATIcourses.com or call ATI at 888.501.2100 or 410.956.8805
  3. 3. e e at at lic l ia om lic up er .c up at D es D IM ot rs ot N om AT ou N o Ic o D .c • AT l • D l ia www.ATIcourses.com es te l• er rs a ia w. a ic at om er w ri ou pl M w ate .c at Ic u TI D es M M Boost Your Skills •A ot rs TI 349 Berkshire Drive I AT w. N ou A te Riva, Maryland 21140 AT with On-Site Courses w Do Ic te • .c ca Telephone 1-888-501-2100 / (410) 965-8805 te om es li ca l• om a rs up Tailored to Your Needs Fax (410) 956-5785 w .c lic ia w. li ou D Email: ATI@ATIcourses.com w up er es up AT Ic ot at w D rs D AT N M The Applied Technology Institute specializes in training programs for technical professionals. Our courses keep you ot ou ot o current in the state-of-the-art technology that is essential to keep your company on the cutting edge in today’s highly N I Ic N w. D AT competitive marketplace. Since 1984, ATI has earned the trust of training departments nationwide, and has presented o AT Do l• D on-site training at the major Navy, Air Force and NASA centers, and for a large number of contractors. Our training ia l• increases effectiveness and productivity. Learn from the proven best. w. • er w ial ia w at er w er For a Free On-Site Quote Visit Us At: http://www.ATIcourses.com/free_onsite_quote.asp IM at at IM AT IM For Our Current Public Course Schedule Go To: http://www.ATIcourses.com/schedule.htm w AT AT
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  5. 5. Remote Sensing Satellites and Information Extraction  Instruction provided by; Applied Technology Institute www. ATIcourses.com ATI@ATIcourses.co page 2
  6. 6. Instructor  Barry Haack  George Mason University  Department of Geography and Geoinformation Science  MSN 6C3  Fairfax, VA 22030  Phone 703 993 1215  E-mail bhaack@gmu.edu page 3
  7. 7. Objectives and Outline  Definitionsvocabularyconcepts of RS  Current status of satellite RS  Information extraction methods RS  Remote sensing links with GIS  Case studies page 4
  8. 8. Case Studies  Omo River Delta Growth – Kenya  Agriculture and Change – Afghanistan  Mapping and Monitoring Urban Growth – Nepal  Land Use Mapping and Change – Mt. Everest  Ratio Estimation for Rice – Bangladesh  Radar and Optical Data Fusion – Sudan, Nepal page 5
  9. 9. Remote Sensing  Collection of information without direct contact  Remote sensing primary source of spatial data  Maintains a historical record of the Earth’s surface  Provides current information  Allows for change detection and predictive models page 6
  10. 10. RS Information Extraction Methods  Visual/manual/photographic/optical from hard or soft copy products  Digital/numerical/computer/quantitative Image enhancement Automated classification  Some hybrid or combination techniques  “Art and science of remote sensing information extraction” page 7
  11. 11. Remote Sensing Roles  Base maps photogrammetric considerations generally air photo based (hyperspatial - spaceborne) great spatial detail contours, transportation, buildings, utilities  Thematic information single or multiple classes often spatially generalized focus of this workshop page 8
  12. 12. Air Photo Derived Base Map page 9
  13. 13. Major Issues RS Integration to GIS  Geometric rectification to coordinate system  Cartographic generalization - scale compatibility  Data structure (raster - vector)  Error - accuracy page 10
  14. 14. Resolution in Remote Sensing  Spatial, degree of spatial detail, meters, pixel size  Spectral, number and types of energy - wavelengths  Temporal, frequency of acquisition, days or hours  Radiometric, discrimination in energy recorded (bits)  Concept of resolutions useful for remote sensing data evaluation data specifications for informational needs 11 page
  15. 15. Spatial Resolution page 12
  16. 16. Remote Sensing Platform  Height above surface  Airborne or spaceborne  Historically tradeoff - footprint and spatial resolution low altitude, small footprint -large spatial detail high altitude, synoptic view - low spatial detail  Exceptions in national assetsintelligence data and recent spaceborne systems page 13
  17. 17. Remote Sensing Platform Tradeoff; Spatial Resolution vs Footprint/Synoptic Coverage page 14
  18. 18. Electromagnetic Spectrum  Classified by wavelength and frequency  Inverse relationship - wavelength and frequency  Wavelengths in micrometers (one one-millionth meter)  Reflected or emitted energy .04 .4 .5 .6 .7 1.5 4.5 300 1m ultraviolet visible infrared microwave B G R near mid thermal radar 15 page
  19. 19. Electromagnetic Spectrum page 16
  20. 20. Energy Flow Profile  Energy source  Source to surface  Interaction at surface  Surface to sensor  Sensor to user page 17
  21. 21. Various Paths of Satellite Received Radiance Remote sensor detector Total radiance L at the sensor S Solar E irradiance 0 90Þ Lp LT Components T Of EFP; 0 2 T v Wavelength, Diffuse sky irradiance Ed 1 1,3,5 Atmosphere Time and 4 v Location 3 0 LI Dependent 5 Reflectance from Reflectance from neighboring area, study area, r r n page 18
  22. 22. Selected Spectral Signatures- Reflectance Curves page 19
  23. 23. Signature Extension Problem  Signatures are highly variable  Signatures may not be unique  Signatures may be too unique  Mixed pixel problem (mixel)  Signatures can not be extended over time or space page 20
  24. 24. Operational Spaceborne Remote Sensing - Classes  Medium spatial resolution multispectral (10 to 100m)  Radar  High spatial resolution (<10 m)  Low spatial resolution multispectral (>100 m) includes meteorological  Hyperspectral page 21
  25. 25. Landsat Orbit Parameters  570 mile or 920 km height  16 to 18 day repeat coverage  Near polar NE to SW orbit  81 north to 81 south  Sun synchronous 9:30 am  Archived by global path/row location  All data free from USGS – EROS since January 2009 (~1,000,000 frames distributed) page 22
  26. 26. Landsat Thematic Mapper TM  Since 1982, Landsats 4 and 5  Seven spectral bands, VB,VG,VR,NIR, MIR, TIR,MIR  30 meter pixel, 120 m TIR  256, 8 bit radiometric resolution Enhanced Thematic Mapper ETM+ Landsat 7 1999 Seven bands Panchromatic band at 15m System difficulties, May 2003, Landsat Data Continuity Mission LDCM (2011)/Data Gap? page 23
  27. 27. Landsat TM Subset 154 BGR Cairo page 24
  28. 28. SPOT  French, Five since 1986, Linear array or push broom  SPOTs 1 to 3  10 m panchromatic, 20 m three band multispectral  60 by 60 km format  Pointable sensor, stereo - greater temporal resolution  SPOT 4 1998 Added fourth MSS band (Mid IR 1.5 to 1.75)  SPOT 5, 2002 2.5 and 5 m panchromatic at 60 km swath Vegetation mapper on 4 and 5 at 1km. Daily page 25
  29. 29. ASTER  US and Japan, 1999, research, Terra platform  Advanced Spaceborne Thermal Emission and Reflection Radiometer  14 Bands, three visible/NIR, 15 m  six SWIR/MIR, 30 m  five TIR, 90 m  60 km swath  5 day temporal resolution in vis/NIR  stereo possible, DEM  Archive exists, on-demand instrument page 26
  30. 30. Advantages of Radar  Day and night  Weather independent /cloud penetration  Vegetation and surface penetration  Determine distance IFSAR DEM  SLAR Side Looking Airborne Radar  SAR Synthetic Aperture Radar page 27
  31. 31. RADARSAT  Canadian  4 November 1995 launch RADARSAT 1  C-band, 5.6 cm, HH polarization  Programmable incident angle, spatial resolution, and swath/footprint  Spatial resolution from 8 to 100 m  Footprint from 50 x 50 km to 500 x 500 km  RADARSAT 2, 2008, Quad Polarization page 28
  32. 32. Fine Spatial Resolution (< 10 m) Hyperspatial  GeoEye IKONOS, 1999 .8 m panchromatic, 3.2 m three band MSS 11 x 11 km footprint, 3-5 day temporal GeoEye 1, September 2008 .41 m pan, 1.6 m MSS (3 bands), 15.2 km  Digital Globe - QuickBird, 2001 0.6 m pan and 2.6 m MSS,1-3.5 days, 16.5 km WorldView=1, 2007 0.5 m pan, 11 bit, 1.7 day revisit, 17.6 km  SPOT 5, 2002 2.5 and 5 m panchromatic, 60 km page 29  Variable costs, archive vs new acquisition,~$25 sq km
  33. 33. Baudhanath Stupa, Nepal Corona 1967 IKONOS 2001 page 30
  34. 34. Statistical Nature of Digital Remote Sensing Data One value per band per pixel MSS scene – 30 MB TM scene – 290 MB File value vs look up table value Band histograms and statistics Spectral signature matching page 31
  35. 35. Major Issues RS Information Extraction - Integration to GIS  Geometric rectification to coordinate system  Cartographic generalization - scale compatibility  Data structure (raster - vector)  Error - accuracy page 32
  36. 36. Visual Image Interpretation  Geometric correction before or after interpretation creation of mosaic/image maps  Classification system (single or multiple classes)  Class definitions  Minimum mapping unit (MMU)  Hardcopy or softcopy data sources  Conversion to GIS - direct digital, digitizing, scanning  Accuracy assessment page 33
  37. 37. Land Use/Land Cover; Kathmandu, Nepal page 34
  38. 38. Issues of Automated Classification  Normally based only on pixel by pixel values  No context/site/situation which is strength of visual  Only use digital if visual inadequate  Not necessarily more accurate or objective page 35
  39. 39. Atmospheric Compensation  Variations in Energy Flow Profile  Within scene or between scenes  Signature extension problem; spatial and temporal  Match sensor data to known reflectance curves  Match imagery over time and space  Very difficult to do effectively  Often not necessary and simply ignored (extract signature from scene) page 36
  40. 40. Initial Statistical Evaluation  Full study area for display, often sampling  Digital Numbers (DN)  Display is normally of stretched data (file vs look-up table)  Assume normal distribution of data, often is not normal  Histograms (often bi and multimodal)  Count zeros or not in statistics?  File (upper left origin) or Map (lower left origin) coordinates  Basic statistics; mean, standard deviation, minimum, maximum  Multivariate measures; Variance and co-variance, correlations page 37
  41. 41. Sample Scene Statistics Landsat TM , Charleston South Carolina Band 1 2 3 4 5 7 6 Mean 65 26 24 27 32 15 111 Std.Dev 10 6 8 16 24 12 4 Min 51 17 14 4 0 0 90` Max 242 115 131 105 193 128 130 page 38
  42. 42. Geometric Rectification (1)  Often can be vendor supplied  Registration to other data (scene to scene, no coordinate base)  Rectification to coordinate system  Two or three dimensional (often two dimensional, ortho X, Y and Z)  Select coordinate system (UTM, Lat/Long, State Plane)  Select geoid datum; NAD27,NAD83, WGS84 etc.  Use of Ground Control Points (GCPs) Sources; base map, other image, GPS  Select order of transformation (First, Second, Third, etc.) First order adequate for Landsat Second order for off-nadir such as SPOT Third and higher, rubber sheeting for greater distortions page 39
  43. 43. Geometric 2  Evaluate transformation based on Root Mean Square (RMS) error Overall and per point, measured in pixel resolution RMS under 1 desirable and possible Options to reduce high RMS Delete GGPs Add GCPs Increase order of transformation  Balance order, GCPs and RMS Fewer GCPs always better RMS  Apply transformation, change pixel size, spatial resolutionpage 40 Radiometric resampling
  44. 44. Automated Classification -1  Signature matching process  Pixel or object oriented  Difficulties signature not unique for given sensor signature too unique (10 corn fields, 10 signatures) mixed pixels (unmixing with simple covers) atmospheric changes, signature extension issue page 41
  45. 45. Automated Classification -2  Signature extraction training sites or supervised clustering or unsupervised  Application of a decision rule  Accuracy assessment  Spatial filtering for GIS compatibility page 42
  46. 46. Signature Extraction  Most important aspect, poor signatures always poor results (Garbage in – Garbage out)  From analysis data set  Possibly stratify study area  Supervised or unsupervised page 43
  47. 47. Supervised Signatures  Training (Calibration) sites (Areas of Interest AOI)  Prior knowledge of data  Multiple sites per class  Minimum size (10 x number of bands) normally much larger  Use of seed pixel with spatial and spectral constraints page 44
  48. 48. Unsupervised Signatuure Extraction or Clustering  Locates pixels of similar spectral characteristics  Analyst defined number of clusters  Minimum three times number of expected cover types  Sometimes hundreds (splitters or lumpers)  Many clusters insignificant or mixed pixels  Analyst must identify class for each cluster  Hybrid (combination of supervised and unsupervised) page 45
  49. 49. Spectral Signatures Landsat B G R NIR MIR MIR Urban 71 29 30 37 56 28 Std Dev 7 4 6 5 11 7 Forest 57 22 19 39 36 13 Std Dev 2 1 1 5 6 3 Wetland 59 22 20 20 28 12 Std Dev 2 1 1 2 4 2 Water 62 23 18 9 5 3 Std Dev 1 1 1 1 1 1 page 46
  50. 50. Accuracy Assessment (1)  Locational and thematic  Extremely important - visual and digital extraction  Spatial data without accuracy of questionable value  Accuracy should be a component of metadata  Very difficult and often avoided, embarrassing  Expensive page 47
  51. 51. Accuracy Assessment (2)  Temporal differences often a constraint  Classification the most difficult to evaluate, definitional in part  Major difficulty is identification of ‘truth’ (Validation)  Best if validation at time of data acquisition  Truth must be different from training sites page 48
  52. 52. Accuracy Assessment 3  Method of accuracy evaluation Points or polygons Sample size (minimum 50 per class?) Sample selection; random, systematic, stratified  Numerous statistical procedures for accuracy Contingency matrix * Errors of omission and commission Producers and users accuracies Kappa coefficient page 49  Less concern statistical procedure, more with truth
  53. 53. Contingency Table Sudan Urban Veg Other Totals Users % Urban 15,248 335 1,502 17,085 89.2 Agriculture 2,012 3,015 1,159 6,186 48.7 Other 934 200 21,961 23,095 95.0 Totals 18,194 3,551 24,622 46,367 Producers % 83.8% 84.9% 89.2% Correctly Identified Pixels 40,225/46,367 = 86.8% page 50
  54. 54. Methods to Improve Information Extraction 1  Change data input Different sensor Different date Multitemporal Multisensor Context, texture Ancillary data, GIS page 51
  55. 55. Methods to Improve Information Extraction 2  Change processing strategies Better signatures Change decision rule, hierarchical Neural networks, AI, expert systems, fuzzy logic, regression trees CART page 52
  56. 56. Conclusions  Multiple RS platforms and sensors in future  Importance of date of RS data and field work  Visual information extraction before digital  Accuracy assessments required  RS and GIS integration is two directional  Art and science of RS, visual and digital  Thank you! page 53
  57. 57. You have enjoyed ATI's preview of Remote Sensing Information Extraction Please post your comments and questions to our blog: http://www.aticourses.com/wordpress-2.7/weblog1/ Sign-up for ATI's monthly Course Schedule Updates : http://www.aticourses.com/email_signup_page.html

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