I have a large data set of RGB satellite data that classifies 64x64 pixel images with a spatial resolution of 10m per pixel into 10 classes (e.g. highway, industrial, river, forest). Now I want to train a model (e.g. using resnet34 with fast.ai) on it. So far my model has a error rate of 4% on the test data.

Now I want to classify chunks of an unseen larger scene. My approach is to iterate over the new image and create n x n pixel subimages that get classified and create a stitched 10-color image out of the classified n x n images.

From a visual standpoint I get good results down to about n=32. Below that the predictions are just wrong. I assume that below that mark the model cannot recognize shapes in the chunks.

My question as a novice in data science are:

  1. Is it even legitimate to classify chunks of n<64 considering that the model was trained on n=64 images
  2. Is there a way to classify my unseen picture with a model built on the dataset on a per-pixel basis (i.e. n=1) or do I have to find a different dataset / create my own dataset?

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.