Random Forest Classification of Landsat8 Image gives invalid/noise output

Question

In my previous question (memoryerror in Supervised Random Forest Classification in Python sklearn) encounter further issue on random forest classification output. The python code runs fine, but the output is noise or invalid classification. I used randomly generated points and collected pixels from Maximum Likelihood Classification from ArcGIS 10.3 which was used as training dataset. The training dataset contains 1500 labels of land use-land cover. I am assuming it could be because point dataset may not contain enough pixels to represent each land-use, but i can't be sure.

Can you explain why the classification output is noise?

EDIT: The apparent noise output was due to switched row, col at the last sections of the code.

Answer 1

You stated:

I used randomly generated points and collected pixels from Maximum Likelihood Classification from ArcGIS 10.3 which was used as training dataset.

I suspect creating training data by sampling the output from a maximum likelihood land cover classification is the source of the noise and error. This approach is not recommended. Fewer, high quality training data is much preferred over lots of poor training data.

A better approach would be to create high quality training data using either of the following approaches:

1. Collect on-the-ground training data and have a balanced sampling design (e.g. equal numbers of training points per class).
2. Expertly select pixels on-screen that represent the correct land cover classes--also using a balanced sampling design.