Results 1 to 10 of about 204,035 (336)
Unsupervised Domain Adaptation With Dense-Based Compaction for Hyperspectral Imagery
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Enormously hard work of label obtaining leads to the lack of enough annotated samples in the hyperspectral imagery (HSI). The mentioned reality inferred the unsupervised classification performance barely satisfactorily.
Chunyan Yu +4 more
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A Spatial-Enhanced LSE-SFIM Algorithm for Hyperspectral and Multispectral Images Fusion
Remote Sensing, 2021 The fusion of a hyperspectral image (HSI) and multispectral image (MSI) can significantly improve the ability of ground target recognition and identification.
Yulei Wang +4 more
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VGC 2023 - Unveiling the dynamic Earth with digital methods : 5th Virtual Geoscience Conference ; Book of Abstracts, 2023 Hyperspectral imaging (HSI) technology is a combination of conventional imaging and spectroscopic techniques, which has a unique ability to simultaneously acquire both the spatial and spectral data of a specimen. The HSI is a novel optical tool in food processing that has a great potential for rapidly identifying bacteria (and possibly other ...
Lorenz, Sandra, Kirsch, Moritz
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A New Deep Convolutional Network for Effective Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Hyperspectral unmixing extracts pure spectral constituents (endmembers) and their corresponding abundance fractions from remotely sensed scenes. Most traditional hyperspectral unmixing methods require the results of other endmember extraction algorithms ...
Xuanwen Tao +7 more
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Orthogonal Subspace Projection Target Detector for Hyperspectral Anomaly Detection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Orthogonal subspace projection (OSP) is a versatile hyperspectral imaging technique which has shown great potential in dimensionality reduction, target detection, spectral unmixing, etc. However, due to its inherent requirement of prior target knowledge,
Chein-I Chang, Hongju Cao, Meiping Song
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HYPERSPECTRAL PANORAMIC IMAGING [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018 Abstract. Hyperspectral instruments are designed for the characterisation of planetary surfaces, oceans and the atmosphere. At the moment there are a number of aircraft systems and planned space missions. Examples for this are the hyperspectral missions for Earth remote sensing (EnMAP) and also for deep space and planetary missions (Mercury mission ...
Müller-Rowold, Malte, Reulke, Ralf
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Nanophotonics, 2023 To comprehensively describe the operation of photo-induced force microscopy (PiFM), we have developed a model based on coupled harmonic oscillators. This model features two point masses connected by massless elastic wires, offering greater intuitiveness ...
Jahng Junghoon, Lee Eun Seong
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Target extraction can provide a prior knowledge for spectral unmixing, unsupervised hyperspectral image classification, and unsupervised target detection tasks, which is of great practice.
Xiaodi Shang +4 more
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Hyperspectral Subspace Identification [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2008 Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage.
Bioucas-Dias, José M., Nascimento, Jose
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Convolutional neural networks (CNN) have led to a successful breakthrough for hyperspectral image classification (HSIC). Due to the intrinsic spatial-spectral specificities of a hyperspectral cube, feature extraction with 3-D convolution operation is a ...
Chunyan Yu +4 more
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