Richard Murphy

High‐resolution hyperspectral imaging of diagenesis and clays in fossil coral reef material: a nondestructive tool for improving environmental and climate reconstructions

Publisher: American Geophysical Union (AGU)

Date: 08-2017

DOI: 10.1002/2017GC006949

Pretraining for Hyperspectral Convolutional Neural Network Classification

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 05-2018

DOI: 10.1109/TGRS.2017.2783886

Unsupervised Feature-Learning for Hyperspectral Data with Autoencoders

Publisher: MDPI AG

Date: 10-04-2019

DOI: 10.3390/RS11070864

Abstract: This paper proposes novel autoencoders for unsupervised feature-learning from hyperspectral data. Hyperspectral data typically have many dimensions and a significant amount of variability such that many data points are required to represent the distribution of the data. This poses challenges for higher-level algorithms which use the hyperspectral data (e.g., those that map the environment). Feature-learning mitigates this by projecting the data into a lower-dimensional space where the important information is either preserved or enhanced. In many applications, the amount of labelled hyperspectral data that can be acquired is limited. Hence, there is a need for feature-learning algorithms to be unsupervised. This work proposes unsupervised techniques that incorporate spectral measures from the remote-sensing literature into the objective functions of autoencoder feature learners. The proposed techniques are evaluated on the separability of their feature spaces as well as on their application as features for a clustering task, where they are compared against other unsupervised feature-learning approaches on several different datasets. The results show that autoencoders using spectral measures outperform those using the standard squared-error objective function for unsupervised hyperspectral feature-learning.

Investigating variations in background response in measurements of downhole natural gamma in a banded iron formation in the Pilbara, Western Australia

Publisher: Elsevier BV

Date: 02-2017

DOI: 10.1016/J.JAPPGEO.2016.12.018

Quantifying leaf-scale variations in water absorption in lettuce from hyperspectral imagery: a laboratory study with implications for measuring leaf water content in the context of precision agriculture

Publisher: Springer Science and Business Media LLC

Date: 20-09-2019

DOI: 10.1007/S11119-018-9610-5

Detecting historical changes to vegetation in a Cambodian protected area using the Landsat TM and ETM+ sensors

Publisher: Elsevier BV

Date: 12-2016

DOI: 10.1016/J.RSE.2016.10.027

University Of Reading

Location: United Kingdom of Great Britain and Northern Ireland

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National Institute Of Water And Atmospheric Research Wellington

Location: New Zealand

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University Of Sydney

Location: Australia

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Industrial Transformation Training Centres - Grant ID: IC170100023

Start Date: 12-2017

End Date: 12-2024

Amount: $4,619,950.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP0559073

Start Date: 03-2005

End Date: 03-2008

Amount: $375,000.00

Funder: Australian Research Council