Classifying With a Score - Conceptual Question #2097
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natedailey7
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RV does have some support for regression but it is limited to chip-level regression i.e. predicting a single number for a full chip, rather for each pixel in the chip. In your case, you could subclass the semantic segmentation learner and dataset classes into RasterRegression* classes with some modifications. You would need to modify the loss function from cross-entropy to MSE or MAE, and modify the So it will take some work, but if you do manage to get something working, I'd appreciate a PR! |
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I'm trying to classify each pixel in an image with a score. That is, I'm trying to create a raster with pixels containing a percentage score. The application is to calculate the percentage canopy cover of a forest based on imagery, with a low score meaning little canopy cover and high score meaning dense canopy cover.
I'm thinking I could create discrete categories for different scores (0-0.1; 0.1-0.2, etc.) and then classify them with the semantic segmentation tools, but I'm wondering if you would recommend an approach that is more continuous. Is this in the realm of possibility for Raster Vision, or should I look elsewhere? Thanks in advance.
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