Triplet-Watershed for Hyperspectral Image Classification

TL;DR

This paper introduces a novel watershed classifier integrated with deep learning for hyperspectral image classification, leveraging connectivity patterns for improved accuracy in supervised and semi-supervised settings.

Contribution

It proposes a trainable deep learning approach to optimize representations for the watershed classifier, achieving state-of-the-art results with fewer parameters.

Findings

State-of-the-art accuracy on multiple hyperspectral datasets

Fewer parameters than previous models

Effective in both supervised and semi-supervised scenarios

Abstract

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.