Extracting trees from image without picking up background vegetation?

Question

I do not have a background in image recognition/feature extraction, but I am in need of a way to extract trees from an image without the background vegetation.

Seen above is a small example of the kind of imagery I'm working with. I have access to multi-spectral imagery as well (though I haven't seen it yet) including NDVI, NIR, Red-edge.

From researching the problem at hand, I am aware that feature extraction is an active area of research and it seems that often supervised and unsupervised machine learning is employed in combination with statistical voodoo such as "PCA". Being able to differentiate between trees and background vegetation has been noted as an area of difficulty in some papers I skimmed over in my research.

There are notable features about the imagery I am working with. First of all, the palm trees have a very distinctive shape. Not only this, but there are obvious differences in the texture of the trees vs the texture of the background vegetation.

I am not an academic, and as such I only have access to publicly available papers for my research. I am looking for relevant algorithms that could help me extract the features of interest to me (trees) that either have an implementation (ideally in C or bindings to C, though I'm aware that it is not a commonly used language in this field) or with publicly available papers/tutorials/sites/etc. detailing the algorithm so that I could implement it myself.

Thanks in advance for any help!

Answer 1

Look into OpenCV, It has a lot of options for supervised/semi supervised Learning methods. As you have mentioned there is a visible texture difference between the tress and background vegetation, a good place for you would be to start would be color based segmentation and evolving it to use textures as well. OpenCV ML tutorial is a good starting point. Moreover you can also combine the NDVI data to create a stronger feature set.