I am looking for best-practice or case studies using R's "mxnet" for the pixel-wise classification of multi-band imagery (RGB, mlutispectral/hyperspectral aerial or satellite remote sensing). Indeed there is bunch of best practise in image tagging (e.g. dogs vs cats in huge image archives like imagenet) where the whole image is classified and usually a lot of training data (or pretrained models) are available. However, I do not find anything concerning pixel-wise image classification/regression, where training data typically is a bit more sparse and applications deal e.g. with land cover categories, objects (like cars, buildings etc.) or biophysical variables (biomass, soil wetness, chlorophyll content etc.).
Asked
Active
Viewed 342 times
2
-
This is usually called semantic segmentation. – Dr. Snoopy Jan 25 '17 at 12:26
1 Answers
1
FCN (Fully Conv Network) is the pixel-based segmentation that seem to fit your need. MXNet has a nice example showing FCN-xs that uses Python, but if you are really into R you should be able to convert it, or just use the pre-trained network that is available in the example.
Hagay Lupesko
- 368
- 1
- 11