Pixel-Wise Classification of High-Resolution Ground-Based Urban Hyperspectral Images with Convolutional Neural Networks

Using ground-based, remote hyperspectral images from 0.4–1.0 micron in ∼850 spectral channels—acquired with the Urban Observatory facility in New York City—we evaluate the use of one-dimensional Convolutional Neural Networks (CNNs) for pixel-level classification and segmentation of built and natural...

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Bibliographic Details

Published in:	Remote Sensing
Main Authors:	Farid Qamar, Gregory Dobler
Format:	Article
Language:	English
Published:	MDPI AG 2020-08-01
Subjects:	hyperspectral image segmentation convolutional neural networks urban science
Online Access:	https://www.mdpi.com/2072-4292/12/16/2540