Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Triplet-Watershed for Hyperspectral Image Classification

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. These results are validated on Indianpines (IP), University of Pavia (UP), and Kennedy Space Center (KSC) datasets, relying on simple convnet architecture using a quarter of parameters compared to previous state-of-the-art networks.
Document type :
Preprints, Working Papers, ...
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03171597
Contributor : Laurent Najman <>
Submitted on : Wednesday, March 17, 2021 - 9:00:30 AM
Last modification on : Thursday, May 27, 2021 - 6:27:31 AM

File

WatershedHyperspectralClassifi...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03171597, version 1

Citation

Aditya Challa, Sravan Danda, B Daya Sagar, Laurent Najman. Triplet-Watershed for Hyperspectral Image Classification. 2021. ⟨hal-03171597v1⟩

Share

Metrics

Record views

32

Files downloads

21