  Most semantic segmentation approaches of Hyperspectral images (HSIs) use and
require preprocessing steps in the form of patching to accurately classify
diversified land cover in remotely sensed images. These approaches use patching
to incorporate the rich neighborhood information in images and exploit the
simplicity and segmentability of the most common HSI datasets. In contrast,
most landmasses in the world consist of overlapping and diffused classes,
making neighborhood information weaker than what is seen in common HSI
datasets. To combat this issue and generalize the segmentation models to more
complex and diverse HSI datasets, in this work, we propose our novel flagship
model: Clustering Ensemble U-Net (CEU-Net). CEU-Net uses the ensemble method to
combine spectral information extracted from convolutional neural network (CNN)
training on a cluster of landscape pixels. Our CEU-Net model outperforms
existing state-of-the-art HSI semantic segmentation methods and gets
competitive performance with and without patching when compared to baseline
models. We highlight CEU-Net's high performance across Botswana, KSC, and
Salinas datasets compared to HybridSN and AeroRIT methods.
