object-based image analysis (obia) -...
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Object-based image analysis (OBIA)
Remote Sensing (GRS-20306)
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Objects in context
What is this?
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Objects in context
Human cognition is able to identify objects within an image and classify them within a certain context Context is given by the presence of other objects and
their spatial/visual characteristics
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Objects in context: examples
Looking for agricultural land and forest
A
B C
D
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Objects in context: examples
Hierarchical cognition
Forest Water
River Lagoon
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Pixel based classification
Pixels by themselves are not able to place objects in context Pixel based classification approaches do not deal well
with the high information content of RS data Much human interaction is required to clean up the
“salt and pepper” effect
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How many trees?
Original Pan Image
Pixel based classification
Object based classification
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Object-based image analysis
Human cognition ability is translated into a computational language Image objects are classified
after the pixel info is compressed into a layer of homogeneous regions Regions are separated if they
are significantly different from adjacent features
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Object-based image analysis
Beyond spectral info (mean, st. dev.), other object attributes are used to classify:
● Size ● Shape (smooth borders, compactness) ● Texture ● Location in relation to other objects (relative
border, surrounded by) ● Object statistics
Other RS and GIS sources can be combined to add more attributes to the objects (Lidar data, satellite images, vector layers, ............)
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Steps
Segmentation: from pixels to vector objects
Definition of the object network (optional)
Classification using spectral and spatial attributes of objects
Export of the results to a vector/raster format for further analysis (i.e. shapefile)
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Segmentation algorithms of eCognition
Chessboard segmentation Quadtree-based segmentation Contrast-split segmentation Multi-resolution segmentation Spectral difference segmentation Multi-threshold segmentation Contrast filter segmentation
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Chessboard segmentation
Parameters ● Object size 10 (squares of 10x10 pixels)
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Quadtree based segmentation
Parameters ● Mode: color ● Scale: 100
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Contrast Split Segmentation
Parameters ● Band: Blue ● Min threshold: 120 (dark), Max threshold 253 (bright)
Single band in grey scale Dark and bright areas
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Multiresolution segmentation
Parameters ● Scale: 15 ● Shape: 0.1 ● Compactness: 0.5
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Multi-resolution concept flow diagram
Scale Parameter Defines the maximum St. Dev. of the homogeneity criteria. The larger the value, the larger the resulting objects
Homogeneity Composed of 4 criteria which define the total relative homogeneity for the resulting objects Digital values
Color = 1 - shape
Criteria work in pairs (equalized to a value of 1)
Shape
Optimizes the resulting objects in regards to smooth borders Smoothness = (1-b* compactness)*shape
Optimizes the resulting objects in regards to the overall compactness Compactness = (b* compactness*shape)
Color
Defines textural homogeneity Shape = Smoothness + Compactness
Compactness
Smoothness
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Compactness and smoothness
Compactness = [object border-length] √(#pixels)
Smoothness = [object border-length] [border-length for the given boundary box]
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Examples of smoothness / compactness
Smoothness Compactness 12/12=1 12/√9=4
12/12=1 12/ √5=5.45
12/10=1.2 12/ √5=5.45
20/20=1 20/ √9=6.6
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Original colour image (example)
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Scale 25 - shape 0.1, compactness 0.5
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Scale 50 - shape 0.1, compactness 0.5
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Scale 50 - shape 0.9, compactness 0.5
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Scale 50 - shape 0.9, compactness 0.1
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Scale 50 - shape 0.9, compactness 0.9
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Each image object uses the homogeneity criterion to determine the best neighbour to merge with
If the first image object's best neighbour (red) does not recognize the first image object (grey) as best neighbour, the algorithm moves on (red arrow) with the second image object finding the best neighbour
This branch-to-branch hopping repeats until mutual best fitting partners are found
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If the homogeneity of the new image object does not exceed the scale parameter, the two partner image objects are merged
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Hierarchical structure
Users can create various levels of data by grouping image objects in different ways Classifications can refer to the sub objects/super
objects of an image
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Classification
Infinite possibilities: Spectral based classification Sophisticate GIS functions (topological relationships,
reshape algorithms, hierarchical analysis)
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Summary
Spatial resolution improvements of Remote Sensing imagery has led into the need of extracting more information from these datasets Object-oriented analysis allows to get information of
features from an image considering contextual information Segmentation is the key process to go from pixels to
objects. It groups homogeneous pixels into meaningful objects using spectral and spatial criteria.
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Summary
Object-oriented analysis allows to build up a hierarchical network of the different objects/entities present in an image. This methodology integrates GIS and remote sensing
tools. Sophisticated GIS functions as well as RS traditional functions can be used for image classification
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Quickbird panchromatic band (60 cm pixel size) - eCognition software -
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Quadtree-based segmentation (scale parameter = 20)
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Apply threshold range for defining tree objects (in green)
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Merge all unclassified pixels
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Remove ‘holes’ in tree crowns and merge
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Minimum crown size is 15 pixels, merge and NDVI threshold
What next ???
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Thank you for your attention
Questions?