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Anomaly detection in hyperspectral imagery: an overview
International Image Processing, Applications and Systems Conference, 2014Interest on anomaly detection for hyperspectral images is increasingly growing the last decades due to the diversity of applications that aims for detecting small distinctive objects dispersed in a large geographic zone, without any prior knowledge about the scene.
Manel Ben Salem +2 more
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Band reduction for hyperspectral imagery processing
SPIE Proceedings, 2010Feature reduction denotes the group of techniques that reduce high dimensional data to a smaller set of components. In remote sensing feature reduction is a preprocessing step to many algorithms intended as a way to reduce the computational complexity and get a better data representation.
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Detection of underwater objects in hyperspectral imagery
2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2016One of the biggest challenges in detecting underwater objects in hyperspectral imagery is that, unlike the land-based case, the observed spectrum of an underwater target is highly dependent on the properties of the surrounding water, as well as the depth of the target. In this paper we present a very general framework for underwater detection.
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The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination
Sensors, 2021Elia Vangi +2 more
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Multiscale Superpixel-Based Fine Classification of Crops in the UAV-Based Hyperspectral Imagery
Remote Sensing, 2022Shuang Tian, Qikai Lu, Lifei Wei
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Hyperspectral imagery classification with deep metric learning
Neurocomputing, 2019Xianghai Cao, Renjie Li, Licheng Jiao
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Mapping of stream microhabitats with high spatial resolution hyperspectral imagery
Journal of Geographical Systems, 2002W Andrew Marcus, Richard Aspinall
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Hyperspectral Imagery Classification Based on Semi-Supervised Broad Learning System
Remote Sensing, 2018Yi Kong, Xuesong Wang, Yuhu Cheng
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