  Hyperspectral images (HSI) consist of rich spatial and spectral information,
which can potentially be used for several applications. However, noise, band
correlations and high dimensionality restrict the applicability of such data.
This is recently addressed using creative deep learning network architectures
such as ResNet, SSRN, and A2S2K. However, the last layer, i.e the
classification layer, remains unchanged and is taken to be the softmax
classifier. In this article, we propose to use a watershed classifier.
Watershed classifier extends the watershed operator from Mathematical
Morphology for classification. In its vanilla form, the watershed classifier
does not have any trainable parameters. In this article, we propose a novel
approach to train deep learning networks to obtain representations suitable for
the watershed classifier. The watershed classifier exploits the connectivity
patterns, a characteristic of HSI datasets, for better inference. We show that
exploiting such characteristics allows the Triplet-Watershed to achieve
state-of-art results in supervised and semi-supervised contexts. These results
are validated on Indianpines (IP), University of Pavia (UP), Kennedy Space
Center (KSC) and University of Houston (UH) datasets, relying on simple convnet
architecture using a quarter of parameters compared to previous
state-of-the-art networks. The source code for reproducing the experiments and
supplementary material (high resolution images) is available at
https://github.com/ac20/TripletWatershed Code.
