Potential of Ant Colony Optimization to Satellite Image Classification

1. Potential of Ant Colony Optimization to Satellite Image Classification Raj P. Divakaran

2. Remote Sensing – The Definition Remote Sensing is defined as the art and science of obtaining information about any object without coming in direct contact with it. The term is associated with the process of obtaining information about the earth.

3. Schematic Diagram Reflectance detected by sensor Incoming Radiation

4. Energy Balance and Spectral Patterns E(I) = E(R) + E(T) + E(A) • Proportion of energy reflected, transmitted or absorbed depends on the feature on the earth. • Same feature has different spectral characteristics at different wavelengths • Different features have distinct “spectral patterns”

5. Spectral Patterns Of Different Features

6. Satellite Image Classification • Involves grouping pixels into distinct labels • Necessary for subsequent analysis of the image • Image Classification Process: • Supervised • Unsupervised

7. Need For Automated Classification Techniques • Very large amount of data being generated • Insufficient skilled manpower • Result: data not utilized to full extent • Need for quick reference to potentially useful imagery

8. ACO • ACO algorithms take inspiration from the coordinated behavior of ant swarms. • ACO algorithms strive to generate intelligent systems by emphasising on emergence, distributed-ness and autonomy • Bottom-up approach

9. How Can ACO Help? • Does not require the user to create training data samples • Does not assume an underlying statistical distribution for the pixel data • Contextual information can be taken into account. i.e., neighbourhood information for a pixel • Could improve robustness because the solution is not hard-coded

10. Kenge – GIS Extension to Swarm • Developed to make GIS layers accessible for agent-based simulations • Multiple layers can be brought into the same Kenge object interface

11. Approach • Create n groups of ants • Make each group search for a particular spectral combination that corresponds to a distinct feature - vegetation, clouds etc. • Allow them to mark a pixel to indicate they have 'classified' a pixel as belonging to a particular category (analogous to trail laying in real ants) • An ant belonging to group 'a' tends to move onto pixels marked as belonging to the same group (analogous to trail following in real ants) • Trails can be strengthened by this method • Multiple ants can lay trails over the same pixel and the pixel is classified as belonging to the group with the highest trail strength

12. Work Done • Created 3 types of ants - vegetation seeking, water seeking and bare soil seeking ants • Assigned variable parallelopipeds (spectral bounds) to water seeking and bare soil seeking ants • Assigned the NDVI factor for vegetation seeking ants • NDVI = Normalized Difference Vegetation Index • NDVI = (NIR – Red) / (NIR + Red) • High values ( values close to 1) mean healthy vegetation

13. Ant Movement Rules • An ant searches for a trail in its immediate neighborhood • If trail exists, it moves to the pixel with the trail mark • Else, moves randomly • If ant “happiness” reaches threshold point exploratory mode is triggered. The ant moves randomly

14. Preliminary Results Original Image After Classification

15. Classification (after 1000 time steps)

16. Preliminary Results (contd.)

17. Plus Points • Ants are able to classify the image! • Able to identify vegetation • Able to identify bare soil

18. Minus Points • Buildings wrongly identified as soil • Shadows mistaken for water bodies

19. Work That Remains To Be Done • Formulate mechanisms to identify urban areas, clouds • Incorporate diffusion of trails • Incorporate recruitment of ants to other groups • Incorporate contextual searching mechanisms (could become vital for delineating urban areas) • Comparison with standard techniques • Comparison with ground data