object based image analysis
DESCRIPTION
A lecture about object-based image analysis in feature extraction.TRANSCRIPT
Object-Based Image AnalysisDominic AlocMelanie Gaspa
Framework
DATAPREPARATION
SEGMENTATION
CLASSIFICATION
FEATUREGENERALIZATION
FINALOUTPUT
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REDGREENBLUE
ImageImage Layer
Image Object
Image Object LevelFeature
Class
TermsAn image is a set of raster image data. An image consists of at least one image layer based on pixels. Each image layer represents a type of information.
REDGREEN
BLUE
Image Image layer
BLUE
Rule SetProcess
Algorithm
TermsImage
Image LayerImage Object
Image Object LevelFeature
Class
An image is a set of raster image data. An image consists of at least one image layer based on pixels. Each image layer represents a type of information.
REDGREEN
BLUE
Image Image layer
BLUE
Rule SetProcess
Algorithm
Segmentation is performed by splitting the image into zoned partial areas of differing characteristics. The segments are called image objects.
TermsImage
Image LayerImage Object
Image Object LevelFeature
Class
Rule SetProcess
Algorithm
TermsImage
Image LayerImage Object
Image Object LevelFeature
Class
Face Example
Image Segmented Image
Segment the image by homogeneity of Red, Green and Blue mean values of RGB layers
Rule SetProcess
Algorithm
TermsImage
Image LayerImage Object
Image Object LevelFeature
Class
Face Example
Image Segmented Image
Segment the image by homogeneity of Oiliness Mean value of Oiliness Layer
Rule SetProcess
Algorithm
TermsImage
Image LayerImage Object
Image Object LevelFeature
Class
Face Example
Image Segmented Image
Segment the image by homogeneity of Wrinkle Mean value of Wrinkle Layer
Rule SetProcess
Algorithm
TermsImage
Image LayerImage Object
Image Object LevelFeature
Class
A scene, representing an image, is segmented into image objects during the process of image analysis. Image objects are organized into image object levels.
Rule SetProcess
Algorithm
TermsImage
Image LayerImage Object
Image Object LevelFeature
Class
Entire Image
Image Object Levels
Pixel
Face
foundation
eyes
blush
Iris, pupil
Face Example
nose
lips
dark
fair nosepink
lipstickred
lipliner
Rule SetProcess
Algorithm
TermsImage
Image LayerImage Object
Image Object LevelFeature
Class
Face Example
Foundation
Eyes
Nose
Lips
Pixel
Image Object Level 1
Image
Rule SetProcess
Algorithm
TermsImage
Image LayerImage Object
Image Object LevelFeature
Class
A feature is an attribute that represents certain information concerning objects of interest (i.e., measurements, attached data or values).
Rule SetProcess
Algorithm
TermsImage
Image LayerImage Object
Image Object LevelFeature
Class
A class is a category of image objects. It can both be used to simply label image objects or to describe its semantic meaning. Classification is a procedure that associates image objects with an appropriate class labeled by a name and a color.
Rule SetProcess
Algorithm
TermsImage
Image LayerImage Object
Image Object LevelFeature
Class
Rule SetProcess
Algorithm
Assign class based on RGB mean values classification.
Face Example
Level 1
Level 2
TermsImage
Image LayerImage Object
Image Object LevelFeature
Class
Rule SetProcess
AlgorithmSegmented
ImageClassified
segmented image
Face ExampleAssign class for oily and not oily classification
TermsImage
Image LayerImage Object
Image Object LevelFeature
Class
Rule Set
ProcessAlgorithm
Rule Set is a set of processes that is stored in the ‘Process Tree’ window.
Rule Set
Process Sequence
Single Process
TermsImage
Image LayerImage Object
Image Object LevelFeature
Class
Rule Set
ProcessAlgorithm
Processes are the main working tools for developing rule sets.
1.Single process2.Process sequence
TermsImage
Image LayerImage Object
Image Object LevelFeature
Class
Rule Set
ProcessAlgorithm
The algorithm defines the operation the process will perform.
Process related operation Segmentation algorithms Basic Classification algorithms Advanced Classification algorithms Variables operation algorithms Reshaping algorithms Level operation algorithms Interactive operations algorithms Sample operation algorithms Image layer operation algorithms Thematic layer operation algorithms Export algorithms Workspace automation algorithms
End. Thank you.
Object-Based Image Analysis
Segmentation
Classification
Exportation
DITCH EXTRACTIONAn object-oriented approachDITCHDefinition
Ditch- A long narrow excavation designed or intended to collect and drain off surface water.(Road Watch Project: Procedures Manual for Road Construction and Maintenance Ver. 2.1, August 2008)
- An artificial open channel or waterway usually constructed parallel to the dike to drain the overflow or seepage water from the river.(DPWH Technical Standards and Guidelines for Planning and Design, March 2002)
Types of Ditches
irrigation ditch drainage ditch
roadside ditch
BACKGROUND1) Limited number of literatures
2) Available literatures are not detailed enough
3) No generic methodology (Bailly, 2007)
BACKGROUND
1) Applicable to plain area only
2) Assumed to extract ditches with at least 1m in width
Scope and Limitation
General rules 1.Begin with an end in mind.
Develop strategy!2.Dwell on the class/es that needs
to be classified. 3.Think of layers that best
segment/classify the desired class/es
What are the properties of ditch?
consider geomorphological characteristics
RULE SET DEVELOPMENT
Get the big picture
Choose, Process, and Import Data
Translate Strategy into Rule Set
Review Result
Refine/Expand Strategy
Ready for Export
Framework
DATAPREPARATION
FEATUREGENERALIZATION
FINALOUTPUT1
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3
4
5
REDGREENBLUE
CLASSIFICATION
SEGMENTATION
RULE SET DEVELOPMENT
Get the big picture
Choose, Process, and Import Data
Translate Strategy into Rule Set
Review Result
Refine/Expand Strategy
Ready for Export
Workflow
RULE SET DEVELOPMENT
Get the big picture
Choose, Process, and Import Data
Translate Strategy into Rule Set
Review Result
Refine/Expand Strategy
Ready for Export
Workflow
RULE SET DEVELOPMENT
Get the big picture
Choose, Process, and Import Data
Translate Strategy into Rule Set
Review Result
Refine/Expand Strategy
Ready for Export
Choosing Layers
bare earth
Curvature
Digital Terrain Model
is the second derivative of a surface or the slope of the slope.
RULE SET DEVELOPMENT
Get the big picture
Choose, Process, and Import Data
Translate Strategy into Rule Set
Review Result
Refine/Expand Strategy
Ready for Export
Translate Strategy into Rule Set
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RULE SET DEVELOPMENT
Get the big picture
Choose, Process, and Import Data
Translate Strategy into Rule Set
Review Result
Refine/Expand Strategy
Ready for Export
Tiled Processes of Ditch Extraction
An Object-Based Approach For Wetland Mapping Using Seath Algorithm
Wetlands are important.Wetlands are those areas that are inundated
or saturated by surface or ground water at a frequency and duration to support and under normal circumstances do support, a prevalence of vegetation typically adapted for life in saturated conditions. --Ramsar Organization
Wetland Types123456789101112131415
Highland LakeSwampsPeatlandWater Impound (Rice Terraces)MarshRiverIrrigationFishpondLakeReservoirEstuariesTidal FlatsMangrove ForestSeagrass BedsCoral Reefs
Study AreaPaitan Lake
in Cuyapo, Nueva Ecija
General rules 1.Begin with an end in mind. 2.Dwell on the class/es that needs
to be classified. 3.Think of layers that best
segment/classify the desired class/es
RULE SET DEVELOPMENT
Get the big picture
Choose, Process, and Import Data
Translate Strategy into Rule Set
Review Result
Refine/Expand Strategy
Ready for Export
Feature Selection Method
Support Vector Machine
Classification Tree Analysis
Feature Space
Optimization
Separability and
Threshold
Get the big picture
Separability and Threshold (Seath) Algorithm
𝑩=18
(𝒎1−𝒎2 )2 2𝝈1
2+𝝈22+12𝒍𝒏[𝝈1
2+𝝈22
2𝝈1𝝈2]
𝑱=𝟐 (𝟏−𝒆− 𝑩)
Separability
Threshold
Framework
DATAPREPARATION
FEATUREGENERALIZATION
FINALOUTPUT1
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SEGMENTATION
CLASSIFICATION
Flowchart on Wetland Extraction
DSM
DTM
AerialIntensi
tyIntensity -
GLCM
nDSM
Multiresolution Segmentation
Manual Classification of training samples
Selection of Object Features
Exportation of Object Statistics
Object Statistics
Feature Selection and Threshold
CTI
Classification of wetland and non-
wetland
Clean-up operation
Wetland Shapefile
Process/Performed in: ArcGIS ArcGIS, ENVI ArcgGIS, LasTools Seath eCognition
RULE SET DEVELOPMENT
Get the big picture
Choose, Process, and Import Data
Translate Strategy into Rule Set
Review Result
Refine/Expand Strategy
Ready for Export
orthophotograph or orthoimage; an aerial photograph geometrically corrected ("orthorectified") such that the scale is uniform: the photo has the same lack of distortion as a map. Unlike an uncorrected aerial photograph, an orthophotograph can be used to measure true distances, because it is an accurate representation of the Earth's surface, having been adjusted for topographic relief, lens distortion, and camera tilt.
often used as a generic term for DSMs and DTMs, only representing height information without any further definition about the surface
represents the earth's surface and includes all objects on it
represents the earth's surface and excludes all objects on it
the terrain is everywhere set to a standard of zero. The NDSM is accordingly generated by subtracting the Digital Terrain Model (DTM) from the digital surface model (DSM).
Orthophoto
Digital Elevation Model
Digital Surface Model
Digital Terrain Model
normalized Digital Surface Model (nDSM)
Choosing Layers
RULE SET DEVELOPMENT
Get the big picture
Choose, Process, and Import Data
Translate Strategy into Rule Set
Review Result
Refine/Expand Strategy
Ready for Export
Choosing Layersa measure, collected for every point, of the return strength of the laser pulse that generated the point. It is based, in part, on the reflectivity of the object struck by the laser pulse.
A statistical method of examining texture that considers the spatial relationship of pixels is the gray-level co-occurrence matrix (GLCM), also known as the gray-level spatial dependence matrix. The GLCM functions characterize the texture of an image by calculating how often pairs of pixel with specific values and in a specified spatial relationship occur in an image, creating a GLCM, and then extracting statistical measures from this matrix.
Intensity
Gray level Co-Occurrence Matrix (GLCM)
RULE SET DEVELOPMENT
Get the big picture
Choose, Process, and Import Data
Translate Strategy into Rule Set
Review Result
Refine/Expand Strategy
Ready for Export
DSM
Intensity
DTM
Intensity – Gray Level Co-Occurrence Measures
nDSM
Aerial Image
CTI
Processed Layers
Figure 1. Flowchart on wetland extraction
DSM
DTM
Aerial
Intensity
Intensity -
GLCM
nDSM
Multiresolution Segmentation
Manual Classification of training samples
Selection of Object Features
Exportation of Object Statistics
Object Statistics
Feature Selection and Threshold
CTI
Classification of wetland and not
wetland by Nearest Neighbor
Clean-up operation
Wetland Shapefile
Process/Performed in: ArcGIS ArcGIS, ENVI ArcgGIS, LasTools Seath eCognition
RULE SET DEVELOPMENT
Get the big picture
Choose, Process, and Import Data
Translate Strategy into Rule Set
Review Result
Refine/Expand Strategy
Ready for Export
DSM
DTM
Aerial
Intensity
Intensity -
GLCM
nDSM
Multiresolution Segmentation
Selection of Object Features
Manual Classification of training samples
Exportation of Object Statistics
Object Statistics
Feature Selection and Threshold
CTI
Classification of wetland and not
wetland
Clean-up operation
Wetland Shapefile
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2 Not shown in the rule set
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Translate Strategy into Rule Set
NOTE: Algorithms mentioned in literatures are masked into general statements. Decipher.