Object-based image analysis (OBIA)

Remote Sensing (GRS-20306)

Objects in context

What is this?

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

Objects in context: examples

Looking for agricultural land and forest

A

B C

D

Objects in context: examples

Hierarchical cognition

Forest Water

River Lagoon

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

How many trees?

Original Pan Image

Pixel based classification

Object based classification

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

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, ............)

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)

Segmentation algorithms of eCognition

Chessboard segmentation Quadtree-based segmentation Contrast-split segmentation Multi-resolution segmentation Spectral difference segmentation Multi-threshold segmentation Contrast filter segmentation

Chessboard segmentation

Parameters ● Object size 10 (squares of 10x10 pixels)

Quadtree based segmentation

Parameters ● Mode: color ● Scale: 100

Contrast Split Segmentation

Parameters ● Band: Blue ● Min threshold: 120 (dark), Max threshold 253 (bright)

Single band in grey scale Dark and bright areas

Multiresolution segmentation

Parameters ● Scale: 15 ● Shape: 0.1 ● Compactness: 0.5

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

Compactness and smoothness

Compactness = [object border-length] √(#pixels)

Smoothness = [object border-length] [border-length for the given boundary box]

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

Original colour image (example)

Scale 25 - shape 0.1, compactness 0.5

Scale 50 - shape 0.1, compactness 0.5

Scale 50 - shape 0.9, compactness 0.5

Scale 50 - shape 0.9, compactness 0.1

Scale 50 - shape 0.9, compactness 0.9

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

If the homogeneity of the new image object does not exceed the scale parameter, the two partner image objects are merged

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

Classification

Infinite possibilities: Spectral based classification Sophisticate GIS functions (topological relationships,

reshape algorithms, hierarchical analysis)

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.

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

Quickbird panchromatic band (60 cm pixel size) - eCognition software -

Quadtree-based segmentation (scale parameter = 20)

Apply threshold range for defining tree objects (in green)

Merge all unclassified pixels

Remove ‘holes’ in tree crowns and merge

Minimum crown size is 15 pixels, merge and NDVI threshold

What next ???

Thank you for your attention

Questions?