This document summarizes a presentation on using Opticks, an open source remote sensing application, for object-based image analysis. It describes Opticks capabilities for working with imagery data, its plugin architecture, and how it can be used as a development framework. The presentation then outlines Opticks' workflow for object-based image analysis, including image segmentation, object attribution with features, classification using Mahalanobis distance, and vectorization of objects. It provides examples of segmenting and classifying an image and analyzes Opticks' performance at different image sizes. Source code links are also included.

1. OSGEO-India: FOSS4G 2012- First National Conference "Open Source Geospatial Resources
to Spearhead Development and Growth” 25-27th October 2012, @ IIIT Hyderabad
Object Based Image Analysis
Tools for Opticks
Mohit Kumar, KS Rajan, Dustan Adkins
http://osgeo.in/foss4g2012 1

2. OPTICKS ?
• Opticks is an open source, remote sensing application that supports imagery,
video (motion imagery), Synthetic Aperture Radar (SAR), multi-spectral, hyper-
spectral, and other types of remote sensing data.
• Opticks can also be used as a remote sensing software development framework.
Developers can extend Opticks functionality using its plug-in architecture and
public application programming interface
• http://opticks.org
Why object-based?
• Object based approach is better than conventional per-pixel analysis as it deals
with considerably reduced number of units. This approach is not that much
sensitive to noise and hence is spatially consistent.
http://osgeo.in/foss4g2012 2

3. Workflow
http://osgeo.in/foss4g2012 3

4. Image Segmentation (Meanshift)
Input image
(CIELAB colour 5 Dimensional
Input image(RGB) feature space
space)
sing Modes(local
o lla p
space c ect. Clustering
ature ob j
maximas)
in fe form an
i n ts o d e
Po
em
to o n
Objects formed
Pruning (spatial Pruning (Spectral
(backtracking the
Bandwidth) Bandwidth)
modes)
Pruning ( Minimum
region area)
http://osgeo.in/foss4g2012 4

5. Object attribution
• Calculating textural, geometric and spectral features for the objects
made in the Segmentation step in a feature vector.
• Area, Perimeter, Roundness, Compactness, Centroid, Contrast,
Coarseness, Direction, Roughness, Mean red, Mean green, Mean
blue, std. deviation Red, std. deviation Green, std. deviation Blue.
Segmented Image For every object in the image
Initialize a vector having all 16
features
Calculate value for every feature
and save in the vector.
http://osgeo.in/foss4g2012 5

6. Classification
• Mahalanobis Distance
• Di,j2 = (x-µj)`S-1(x-µj)
• The class which has the least Mahalanobis
distance to the object i is the class of that object.
Vectorization
• Creates vector polygons for all connected regions of pixels in the
object image sharing a common pixel value.
• Polygon features are created on the output layer, with polygon
geometries representing the polygons.
• The class which has the least Mahalanobis distance to the object i is
the class of that object.
http://osgeo.in/foss4g2012 6

7. The Input Orbview3 (4m) data of a part of delhi (500X500) The output of the objects with area less than 100.
The shapefile(.shp) displaying the objects
Output of object having area 100-200 and classified as building.
http://osgeo.in/foss4g2012 7

8. Performance Analysis
Image Size Number of Running time
objects (sec)
512 X 512 153 3.1
1024 X 1024 435 24.55
Table 1 : Image segmentation
Image Size Number of objects Running time
(sec)
256 X 256 49 0.249
512 X 512 193 1.133
1024 X1024 752 5.359
2048 X 2048 2965 31.60
4096 X 4096 11922 286.59
Table 2 : Object Attribution
http://osgeo.in/foss4g2012 8

9. Source Code
• https://github.com/mohitkharb/Opticks_GSO
C2012
• http://opticks.org/confluence/display/~mohit
kharb/Workflow+of+the+pluggin
• http://code.google.com/soc/
http://osgeo.in/foss4g2012 9

10. Any Questions?
http://osgeo.in/foss4g2012 10