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Morphological Band Selection for Hyperspectral Imagery
IEEE Geoscience and Remote Sensing Letters, 2018In this letter, a novel morphological band selection method is proposed to obtain the most representative bands from hyperspectral image (HSI) in an unsupervised manner. In order to sufficiently process the HSI, we propose to use only a small set of data instead of using the original full data.
Wang, Jingyu +4 more
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Toward the Vectorization of Hyperspectral Imagery
IEEE Transactions on Geoscience and Remote Sensing, 2023Leyuan Fang +3 more
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Anomaly discrimination and classification for hyperspectral imagery
2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2015Anomaly detection finds data samples whose signatures are spectrally distinct from their surrounding data samples. Unfortunately, it generally cannot discriminate its detected anomalies one from another. One common approach is to measure closeness of spectral characteristics among detected anomalies to determine if the detected anomalies are actually ...
Li-Chien Lee, Drew Paylor, Chein-I Chang
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Separability between pedestrians in hyperspectral imagery
Applied Optics, 2013The popularity of hyperspectral imaging (HSI) in remote sensing continues to lead to it being adapted in novel ways to overcome challenging imaging problems. This paper reports on research efforts exploring the phenomenology of using HSI as an aid in detecting and tracking human pedestrians.
Jared, Herweg +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, 2021Saverio Francini +2 more
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Anomaly detection and compensation for hyperspectral imagery.
2005Hyperspectral sensors observe hundreds or thousands of narrow contiguous spectral bands. The use of hyperspectral imagery for remote sensing applications is new and promising, yet the characterization and analysis of such data by exploiting both spectral and spatial information have not been extensively investigated thus far.
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HTD-Net: A Deep Convolutional Neural Network for Target Detection in Hyperspectral Imagery
Remote Sensing, 2020, Qian Du, Ran Tao
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