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Remote Sensing Applications Supporting Regional Transportation Database Development

Remote Sensing Applications Supporting Regional Transportation Database Development. CLEM 2001 August 6, 2001 Santa Barbara, CA. Chris Chiesa, Chris.Chiesa@Veridian.com (520) 326-7005 ext. 106. Remote Sensing Application Supporting Regional Database for Transportation Planning.

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Remote Sensing Applications Supporting Regional Transportation Database Development

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  1. Remote Sensing Applications Supporting Regional Transportation Database Development CLEM 2001 August 6, 2001 Santa Barbara, CA Chris Chiesa, Chris.Chiesa@Veridian.com (520) 326-7005 ext. 106

  2. Remote Sensing Application Supporting Regional Database for Transportation Planning In Partnership with:

  3. Presentation Overview • Project Summary • Project Objective • Approach • Benefits • Technical Discussion • Land Cover Change Detection and Mapping • Road Feature Characterization and Extraction

  4. Project Objective • Develop tools and methods to facilitate regional transportation road network database development and maintenance • Utilize commercial remote sensing sources to identify and map changes in land use and transportation infrastructure • Automate procedure for extracting and attributing road vectors • Develop procedures within COTS software environment (ERDAS IMAGINE / CAFÉ) • Promote awareness of tools and processes through outreach activities • Training / Workshops • Web-based Interactive Tutorial

  5. Commercial Remote Sensing Sources High-resolution IKONOS LANDSAT Thematic Mapper

  6. Approach • Use multi-date Landsat Thematic Mapper imagery to identify areas within a large region where intensive urban development (hot spots) has occurred. May 26, 1984 Urban development between 1984 and 2000 June 15, 2000

  7. Approach • Acquire high-resolution (IKONOS) imagery over hot spots and enhance road network with one or more spectral features developed for the types of roads present and the geographic environment. Road feature derived from linear combination of IKONOS multi-spectral bands (4-meter) IKONOS panchromatic band (1-meter) IKONOS false color composite (4-meter)

  8. Approach • Extract road locations in newly developed regions and store as vector coverage’s using Veridian’s Lines of Communication (LOC) extraction software.

  9. Approach • Assign attributes (e.g. surface type, width) to vector coverages. 2-lane roads 3-lane roads

  10. Benefits • The LANDSAT program provides an inexpensive means of identifying landcover change over a large area. Landsat Coverage IKONOS Coverage

  11. Benefits • Automated (i.e., user-assisted) road extraction using road spectral features and/or LOC toolkit can be faster, less tedious and less error prone than traditional processing of hand digitizing from aerial photography or satellite imagery. Panchromatic Aerial Photograph Road feature derived from Multispectral Imagery

  12. Change Detection and Feature Extraction Process • Change detection over a large area • Radiometric normalization • Categorize both dates • Categorical change • Radiometric change • Hybrid change • Feature extraction and attribution • Identify regions of intensive development • Generate road features. • Extract road network • Attribute road network

  13. Procedure Overview Date 1 Geo-coded Date 2 Geo-coded Radiometric Normalization Process Categorical Process Categorical Process Date 1 Categorized Image Date 2 Categorized Image Categorical Change Detection Image Categorical Change Process Radiometric Correction Radiometric Change Detection Date 2 Geo- coded and normalized to Date 1 Radiometric Change Detection Process Change Magnitude and Change Direction Hybrid Change Detection Categorical Processing Hybrid Change Detection Process Hybrid Change Product Categorical Change Detection

  14. Acquire Data … • Acquire 2 dates of LANDSAT data • Summer season • Cloud free • Same time of year Mid-Michigan on June 6, 2000 Mid-Michigan on June 8, 1986

  15. Categorize Both Dates… Water Bare ground Vegetation Urban • Label resultant clusters into water, vegetation, bare ground, and urban areas, as appropriate. Unsupervised clustering of Landsat Thematic Mapper image over portion of Michigan on June 6, 2000 Unsupervised clustering of Landsat Thematic Mapper image over portion of Michigan on June 8, 1986.

  16. Categorical Change … Water No data Bare ground Vegetation Urban Urban • Recode categorized files to urban/non-urban.

  17. Categorical Change… Urban on date 1, not on date 2 Urban on both dates No data Urban on date 2, not on date 1 • Combine binary files from both dates to determine where urban changes have occurred. Date1 Date2

  18. Radiometric Change Detection This change magnitude channel shows differences in two dates of Landsat imagery for a region in Michigan. Brighter areas indicate higher magnitudes of change. Often a threshold from this channel is established so that only changes above a certain magnitude will be considered when extracting changes of interest. Increasing difference between pixel values from date 1 to date 2 input images.

  19. Radiometric Change Detection Band 2 Band 3 Band 4 Color Sector Code 0 1 2 3 4 5 6 7 The sector code channel provides information on the “direction” or nature of change. Each color corresponds to a sector code. Each sector code relates to a specific combination of changes observed in image bands as shown in the table above. For example, sector code 6, shown in orange in the image to the left, shows areas that have increased spectral reflectance in bands 2 and 3, and decreased spectral reflectance in band 4. Blue Green Red Near IR

  20. Radiometric Change Detection… • Create a change image composition (CIC) and determine sector codes that best represent urban areas.

  21. Hybrid Change Advantages are: • Labels from categorization • Reduction in false categorical change from CVA Hybrid urban change product of Delta Township in Michigan. Changed areas are annotated in yellow over a Landsat Thematic Mapper False color composite

  22. Feature Extraction and Attribution • Identify geographic locations of localized regions in LANDSAT change product where intensive development has occurred • Generate road features • Extract road network • Attribute road network

  23. Identify Geographic Locations • Identify areas in the Landsat hybrid change product where urban change has occurred and order IKONOS data

  24. Order and Receive Data • Acquire IKONOS data over area of interest IKONOS false color composite with green band displayed in blue, red band displayed in green, and near infrared band displayed in red. IKONOS panchromatic band IKONOS natural color composite with blue band displayed in blue, green band displayed in green, and red band displayed in red.

  25. Generate Road Features… This scatterplot illustrates how different landcover materials can be separated in 2-dimensional space (2 spectral bands). The arrow shows a direction that can be described as a linear combination of these two bands. The dashed line indicates that both concrete and asphalt can be separated from the other materials with this 2-dimensional feature. Often features are created by using multiple bands ( > than 2 dimensions)

  26. Generate Road Features… This plot illustrates how well a specific 4-band spectral feature will work in isolating certain landcover material from other materials in the image. Natural materials are projected towards a categorical value of 1, while man made materials are projected towards a categorical value of 2. The vertical dashed line between these two categories illustrates that this equation will work in separating these 2 categories. In the feature created, man made materials will appear as the brightest objects and natural materials will appear as darker objects. Level slicing the feature at around 150 will separate the two.

  27. Generate Road Features… • Apply coefficients of spectral feature to data and produce road feature. [(Band 1 * -.0256) + (Band 2 * .0915) + (Band 3 * .1346) + (Band 4 * -.2241)] + 148 Adjusts data values into 0-255 range for unsigned 8-bit output Weighted average of satellite raw bands

  28. Generate Road Features… False color composite of IKONOS data displayed with green band in blue, red band in green, and near infrared band in red IKONOS road feature derived from a linear combination of the raw bands

  29. Extract Road Network • Use road feature as input to LOC toolkit and semi-automatically extract roads. • Convert to vector coverage.

  30. Extract Road Network…

  31. Extract Road Network…

  32. Attribute Road Network 3-lane roads 2-lane roads 2-lane roads

  33. Process Summary • Landsat imagery provides broad spatial and temporal coverage over which to observe land changes • Hybrid change detection offers advantages over traditional post-classification change detection in that it also incorporates important radiometric change information and allows “thresholding” of changes • IKONOS imagery provides high spatial resolution to identify the specific transportation features that constitute the changes observed in Landsat imagery • Using a “Road Feature” helps maximize the differentiability of roads and background classes in the imagery • Semi-automated extraction and labeling tools facilitate the process of developing GIS database layers from these remote sensing sources

  34. Questions? • Please contact: Chris ChiesaVeridian Systems4400 East Broadway, Suite 116Tucson, AZ 85711(520)326-7005 ext. 106chris.chiesa@veridian.comwww.veridian.com

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