Fig. 2
From: CEU-Net: ensemble semantic segmentation of hyperspectral images using clustering
Holistic overview of our CEU-Net model. We first choose a clustering method and k cluster number that is tuned for each dataset based on preliminary experiments shown in Fig. 3. After the unsupervised clustering method separates our training data into k clusters, we train the k sub-U-Nets for each cluster in parallel. Then we cluster our test data using the same clustering model and send each cluster into their respective sub-U-Nets. Then we concatenate the k sub-U-Net predictions on the test data pixels as the overall model accuracy