Currently our expert user classifies locations by viewing the Google earth satellite image of a location. They consider circular areas of a 5km radius at a set zoom level. They evaluate the image for density of buildings or trees in within the circle and assign the location a classification. We get data in the form:
location | Classification (lat-long) | ---------------------------------- 123.123, 12.34 | B 122.123, 12.34 | A 121.123, 12.34 | C 120.123, 12.34 | A 119.123, 12.34 | A
I want to use this as training data to build a system that can automatically classify a location based on the satellite image.
To train the system, I would need to extract relevant parameters from the satellite images. I guess the image parameters would need to be rotation independent. Possible parameters:
- total intensity for RGB values,
- count of edges
I plan on using C# and available AI & image processing libraries (e.g. opencv, encog, AForge) to do implement the training and classification stages.
Some of the image processing tutorials concentrate on object recognition but that's not really what I'm after (I think). It's more about grouping the images into different categories based on more general properties of the image.
Can anyone advise on a) the feasibility of this plan and b) what parameters I should be extracting from the images?