1. Object-based Classification
BOT/GEOG 4211/5211

2. What is an object?
Image objects are groups of contiguous pixels that are similar spectrally, but also in other characters like texture, shape, and context.
Also called image segments; object-oriented classification = segmentation + classification
Units of classification are these objects, rather than individual pixels, though of course pixels are used to create objects

3. From Blaschke 2010: ISPRS J. of Photogramm. And RS 65:2-16.

4. What is object-based classification?
The process of identifying image objects based on their properties (segmentation) and labeling them (classification)
In some ways, like a semi-automated manual classification, because shape, texture, and context are considered
Considers both spectral and spatial information
Relationships between objects are considered
Can potentially overcome some of the issues we discussed with high spatial rez data because it can integrate shadow, etc., into the objects.
Can better handle within-class variation than many per-pixel classification methods.

5. Study area in Iran
From Matinfar et al. 2007
American-Eurasion J. Agric. & Env. Sci 2(4):

6. Advantages
Takes into account spatial autocorrelation among pixels (recognizes that pixels close together are more likely to be similar on the ground, even when spectrally variable)
Handles some of the problems associated with high spatial resolution data depending on the image
Reduces “salt-and-pepper” classifications
Objects are easier to integrate into vector GIS

7. How does it work? Two step process:
Segmentation – defines the image patches (objects)
Guided by constraints and requirements provided by analyst (you!)
Classification – labels each object with its informational class name or groups similar objects
Usually supervised but can be unsupervised
Often use fuzzy classification or other methods (e.g., nearest neighbor classification)
Based on object characteristics compared to training data (or to other objects)
Nearest neighbor classification. Object is put into the class that is most common among its nearest neighbors based on proximity to a training example in feature space. Define how many nearest neighbors to examine. Simple machine learning algorithm.
See Campbell’s Intro to Remote Sensing, 5th edition

8. Segmentation Often hierarchical (multiple nested levels of detail)
Uses various properties to divide image into objects
E.g., pixel values (color), pixel intensity, texture, variance, shape, etc.
These properties can be weighted differently
Many algorithms available for determining boundaries
E.g., region growing, the Fractal Net Evolution Approach (FNEA), simulated annealing, etc.
Requires that the user set segmentation parameters
E.g, scale, shape, color, minimum variance, edge detection parameters, etc.
Region growing: start with seed pixels and grow regions based on chosen criteria (spectral, texture, etc.); FNEA: Similar to previous. Pixels are merged one by one with objects based on “local homogeneity.”; Simulated annealing

9. Level 3 Level 2 Level 1 From Matinfar et al. 2007
American-Eurasion J. Agric. & Env. Sci 2(4):

10. Scale
Scale is the parameter that controls (indirectly) the grain size of the segmentation
Smaller scale number results in smaller objects
From Li et al International Archives of the Photogramm. Rem Sens, and Spatial Info. Sci. 37(B4)

11. Scale parameters 8, 16, and 22 (left to right)
From Li et al International Archives of the Photogramm. Rem Sens, and Spatial Info. Sci. 37(B4)

12. Figure from U. of Texas San Antonio: Laboratory for Rem. Sens
Figure from U. of Texas San Antonio: Laboratory for Rem. Sens. and Geoinformatics

13. Segmentation algorithms
Region growing
Begin with seed pixels and then add neighboring pixels to a growing object based on their characteristics and those of the neighborhood.
Used by Erdas segmentation (Erdas 2011), eCognition, and others
The Fractal Net Evolution Approach (FNEA)
“uses local mutual best-fit heuristics to find the least heterogeneous merge in a local vicinity following the gradient of best fit” (Blaschke et al. 2007)
Goal is to build objects that are homogeneous in terms of multiple characteristics, like texture and intensity.
Simulated annealing
Name from annealing metals which involves heating and slow cooling to increase crystal size and reduce defects
In segmentation, analog is slowly reducing the tolerance for bad solutions to the object forming process. Algorithm tries many solutions and eliminates the ones that don’t maximize quality of result.

14. Classification
Once the objects are created (segmented), they are classified (similar objects put into groups)
Typically use fuzzy or nearest neighbor methods to classify objects into groups.
Fuzzy methods assign probability of membership of all objects to all classes: 0 = no probability of membership, 1 = 100% probability.
Nearest neighbor is like a distance-based feature space classifier. Objects are grouped with types based on distance from training data and similarity to neighbors.

15. In k-nearest neighbor classification, if k=3 green dot grouped with triangles. If k = 5, green dot grouped as squares, where the triangles and squares are training data.
From Wikipedia:

16. Software eCognition Erdas ENVI Spring Caesar
Many others—some specifically for RS, others not.