An Object Oriented Algorithm for Extracting Geographic Information from Remotely Sensed Data
Zachary J. Bortolot
Assistant Professor of Geography
Department of Integrated Science and Technology
BE the CHANGE
Texture-based vs. Object-based image processing
Texture based Object based
Basis of analysisStatistical calculations performed on pixels in a moving window
Objects (connected pixels representing features) identified in the image
Integration with ground data
Easy Difficult
Performance when objects difficult to recognize
Variable Poor
Suitability for extracting attribute data
Good Often poor (except for location)
Utilization of known properties of features of interest
Poor Good
Objective:
To create an object oriented algorithm that incorporates the strengths of the texture-based approach
To meet this objective the Blacklight algorithm was created
Three versions exist:
-Three band passive optical
-Panchromatic passive optical
-Three band passive optical plus LiDAR
The project setup window for the version that uses LiDAR and passive optical imagery
The data in the spreadsheet consists of data on the phenomena you would like to map made on the ground using a GPS unit or through image interpretation.
In this case the data are trees / hectare for a forest.
Next, the user uses sliders to identify objects he or she thinks may be related to the attribute of interest.
The sliders work by creating a linear equation based on a series of images created using simple image processing operations.
This equation should maximize the response to the object, and minimize the response to the background.
If the equation value is greater than 0, a pixel is considered to be part of the object.
In this case the equation is:
1.17DN2.0DN0.1DN2.0DN0.1 if 1otherwise 0
heightedcontrast_ralbedored{ Threshold
Once the objects have been initially identified, metrics can be calculated based on the object.
For example:
The percentage of the plot taken up by objects.
The percentage of the object pixels that are core pixels.
These metrics are used in a regression equation to predict the measured attribute.
= Core pixel
Percent core = 12.5%
To improve the prediction accuracy, an optimization procedure is run which adjusts the sliders.
The values image.
In this case the number of trees per hectare in the area under the crosshairs is 1810.47.
A map showing the phenomena over the whole area of interest. Clicking on a pixel will bring up the estimated value at that location.
Tests
Test 1: Mapping forests
Test 2: Mapping urban features
Test 3: Mapping population density
Test 4: Mapping vehicle density
Test 1: Mapping forests
Remotely sensed data: 0.5m color infrared orthophotograph
Normalized DSM with a 1m resolution, obtained from DATIS II LiDAR data with a 1m point spacing.
Reference data: 10 circular plots with a 15 m radius placed in 11 – 16 year old non-intensively managed loblolly pine plantations at the Appomattox-Buckingham State Forest in Central Virginia. The following values were measured:
Trees per hectare
Biomass
Plot data
Attribute Minimum Maximum MeanStandard deviation
Trees per hectare 1118 1966 1566 359
Biomass (Mg / ha) 61 136 90 22
Predicted attribute R2 RMSE
Trees per hectare 0.79 164 (10%)
Biomass 0.64 13 (14%)
Results
Trees per hectare
This map was produced by averaging the predicted values in each stand.
This map was produced by segmenting the predicted biomass output from Blacklight using the SARSEG module in PCI Geomatica.
Biomass
This map was produced by averaging the predicted values in each stand.
This map was produced by segmenting the output from Blacklight using the SARSEG module in PCI Geomatica.
Test 2: Mapping the urban environment
Imagery: 1m normal color USDA NAIP data of Morehead, Kentucky from 2004.
Reference data: 25 randomly selected 100 x 100m plots in which the following were calculated based on photointerpretation:
Percent impervious
Percent tree cover
Percent grass
Photointerpreted data
Attribute Minimum Maximum MeanStandard deviation
Percent impervious 0.0 99.1 32.6 34.4
Percent forest 0.0 100.0 54.0 41.7
Percent grass 0.0 51.5 7.4 12.1
Predicted attribute R2 RMSE
Percent impervious 0.94 8.13
Percent forest 0.94 10.52
Percent grass 0.86 4.48
Results
Percent impervious
The values image.
In this case 91.7% of the cell is estimated to contain impervious surfaces.
Percent forest
Percent grass
Test 3: Population density
Imagery: 1m normal color USDA NAIP data of Harrisonburg, VA from 2003.
Reference data: US Census data from 2000. 20 census blocks were randomly selected and 50 x 50m areas at the center of each plot were used for processing.
Mapping population density would be of use in developing countries with no recent, reliable census data.
Reference data
Attribute Minimum Maximum MeanStandard deviation
People / km2 0 3699 1091 1204
Predicted attribute R2 RMSE
People / km2 0.66 707 (65%)
Results
Population density
This map was produced by averaging the predicted values in each census tract.
This map was produced by segmenting the output from Blacklight using the SARSEG module in PCI Geomatica.
Test 4: Vehicle density
Imagery: 6” normal color Virginia Base Map Program data of Harrisonburg, VA from 2006.
Reference data: Photointerpreted vehicles per acre
Photointerpreted data
Attribute Minimum Maximum MeanStandard deviation
Vehicles / acre 0 44 8 13
Predicted attribute R2 RMSE
Vehicles / acre 0.55 8.9 (111%)
Results
Population density
Conclusions
The algorithm shows promise in multiple types of analysis
Planned improvements:
Additional image processing functions
Better LiDAR integration
Additional object metrics
Ability to select metrics based on a stepwise approach
Would you like to test Blacklight?
If so, I would like to hear from you!
Zachary J. Bortolot
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