Object-based image analysis (OBIA) groups pixels into objects and then classifies those objects rather than individual pixels. It uses both spectral and spatial/textural attributes of pixels within objects to improve accuracy over traditional pixel-based classification. ENVI software provides a tool for OBIA that allows image segmentation, attribute computation, rule-based or supervised classification of objects, and export of classified vectors.

1. What is Object-Based Image
Analysis?
Kirk Benell
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2. Object-Based Image Analysis
What is an object?
• An object is a region of interest
with spatial, spectral (brightness
and color), and/or texture
characteristics that define the
region
• Pixels are grouped into objects,
instead of single pixel analysis
• May provide increased accuracy
and detail for classification
purposes
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3. Object-Based Image Analysis
Traditional pixel-based classification
• Based on reflectance values of pixels
• Works for low and medium resolution imagery
• Works for mass area-based features
• Multispectral or hyperspectral imagery
Limitations of pixel-based analysis
• Only spectral, seldom spatial and contextual
• Results with inconsistent salt-and-pepper noise
• Inaccurate borders for texture computation
• Limited extraction of small-scale objects
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4. Pixel-based Classification 1.0
Water
Pixels
Image 0.5
6
5
4
3
2 0.0
1
1.0
Veg
Reflectance
0.5
0.0
1.0
Soil
0.5
0.0
1 2 3 4 5 6
Band
Group materials based on their reflectance
response per pixel
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5. Object-Based Image Analysis
Image Segmented Merged
Feature
Pixels Objects Segmented The Letter ‘E’
Objects
• Group contiguous pixels into objects
• Objects are classified into feature classes based on their
spatial, textural and spectral attributes
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6. Object-Based Image Analysis
• Greater accuracy from input: tone, color, texture, shape, size,
orientation, pattern, shadow, situations
• Advanced visualizations: Computer vision technique using
image segmentation
• Use homogeneous regions as basic analysis elements
• Additional spatial, contextual and semantic information
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7. ENVI Feature Extraction
• Uses an object-based approach to classify imagery
• The ENVI tool provides an easy to use method for extracting
information from panchromatic, multispectral, hyperspectral,
and elevation data
• Vehicles
• Buildings
• Transportation
• Natural Features
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8. ENVI Feature Extraction
Needs for Feature Extraction
• Increased availability of high-
resolution images
• Manual digitization, labor intensive
• Semi-automated solution is highly
desired
Applications
• Defense and Intelligence
• Geographic Information Systems
• Transportation
• Urban planning and mapping
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9. ENVI Feature Extraction
Workflow:
• Spectral/spatial/texture attributes
• Object-based fuzzy rule-based
classification
• Object-based supervised
classification
Preview Window for instant feedback
prior to processing an entire image
Post-Classification Vector Tool
• Centerline extraction
• Snapping, smoothing
• Vector editing
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10. ENVI Feature Extraction
Input Data
Object
Image Segmentation
Generation
Attribute Computation for Object Primitives
Rule Base Feature Selection Object-Based
Classification
Decision Making Supervised Classification
Extracted Features/Classes
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11. Image Segmentation Scale Level
A low scale level provides more A high scale level provides fewer
segments in the final processed image segments in the final processed image
The Preview Window provides on-the-fly
feedback for the selected Scale Level
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12. Segmentation
scale level = 50
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13. Under segemented
scale level = 70
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14. Over segmented
scale level = 30
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15. Merge to aggregate
adjacent segments
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16. Select Classification Method
• Select Classify by
Selecting Examples to
select training data and
perform a supervised
classification
• Select Classify by
Creating Rules to
select attribute
parameters to perform a
classification
• Select Export Vectors
to export without
performing a
classification
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17. View attributes to
characterize feature
of interest
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18. Create rules to define
features of interest
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19. ENVI Feature Extraction
Spatial Attributes
• Region area, length, compactness, convexity, solidity, form factor,
rectangular fit, roundness, elongation, main axis direction, axes length,
number of holes, hole/solidity ratio
Spectral Attributes
• Band minimum, maximum, average and standard deviation
Texture Attributes
• Variance, range, mean, and entropy
Color Space and Band Ratio
• Hue, saturation, intensity, NDVI, NDWI, other ratios
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20. Preview
classification results
and adjust training
data on-the-fly
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21. Export features as one
or individual vectors
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22. View Feature Extraction Report
• View parameters
used and statistics
of exported vectors
• Save as a text
report to share with
colleagues
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23. • Edit vector properties
• View Attribute
Information
• Square-up building
sides
• Smooth vectors
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24. • Push data into ArcMap for
further analysis and vector
editing
• Add imagery and new vector
layer to GIS database
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25. Thank You
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