Unsupervised Feature-Learning for Hyperspectral Data with Autoencoders

Remote Sensing

卷:11

Arman Melkumyan¹  RichardJ. Murphy¹  Anna Chlingaryan¹  Lloyd Windrim¹  Rishi Ramakrishnan² 

[1] Australian Centre for Field Robotics, University of Sydney, Sydney 2006, Australia;

[2] Baymatob Operations Pty Ltd, Leichhardt, Sydney 2040, Australia;

关键词: autoencoders;    unsupervised feature-learning;    hyperspectral;    deep learning;   

DOI  :  10.3390/rs11070864

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown