Fig. 4
From: CEU-Net: ensemble semantic segmentation of hyperspectral images using clustering
Results of the loss weight \(\omega =[\omega _1,\ldots ,\omega _k]^T\) tuning for our ensemble CEU-Net method. Three different weight types are explored: 1) Constant weights in each sub-model, 2) Weights equaling the abundance of data given to each sub-model, and 3) Random weights assigned. All weights have to sum to equal 1 as explained in Eq. 2. All tests were run with 5-fold cross-validation. We observed that the constant weights outperform other methods, therefore, we use constant weights in all of our CEU-Net experiments