Results 21 to 30 of about 35,627 (298)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Compared with land surface temperature (LST) and land surface emissivity (LSE) retrieval from single-band or multispectral thermal infrared (TIR) data, TIR hyperspectral imagery allows us to obtain accurate LST and LSE through the use of an automatic ...
Lyuzhou Gao +4 more
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Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery [PDF]
, 2009 This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing ...
Alfred O. Hero +5 more
core +9 more sources
An Advanced Spectral–Spatial Classification Framework for Hyperspectral Imagery Based on DeepLab v3+
Applied Sciences, 2021 DeepLab v3+ neural network shows excellent performance in semantic segmentation. In this paper, we proposed a segmentation framework based on DeepLab v3+ neural network and applied it to the problem of hyperspectral imagery classification (HSIC).
Yifan Si +7 more
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Remote Sensing, 2017 High-resolution visible remote sensing imagery and thermal infrared hyperspectral imagery are potential data sources for land-cover classification. In this paper, in order to make full use of these two types of imagery, a spatial-spectral-emissivity land-
Yanfei Zhong +4 more
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Deep learning in remote sensing: a review [PDF]
, 2017 Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields ...
Fraundorfer, Friedrich +6 more
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Superpixel Estimation for Hyperspectral Imagery [PDF]
2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2014 In the past decade, there has been a growing need for machine learning and computer vision components (segmentation, classification) in the hyperspectral imaging domain. Due to the complexity and size of hyperspectral imagery and the enormous number of wavelength channels, the need for combining compact representations with image segmentation and ...
Pegah Massoudifar +2 more
openaire +1 more source
Customizing kernel functions for SVM-based hyperspectral image classification [PDF]
, 2008 Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available algorithms.
Baofeng Guo +4 more
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Remote Sensing, 2019 Hyperspectral imagery contains abundant spectral information. Each band contains some specific characteristics closely related to target objects. Therefore, using these characteristics, hyperspectral imagery can be used for anomaly detection.
Kun Tan +4 more
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The Early Detection of the Emerald Ash Borer (EAB) Using Advanced Geospacial Technologies [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014 The objectives of this study were to exploit Light Detection And Ranging (LiDAR) and very high spatial resolution (VHR) data and their synergy with hyperspectral imagery in the early detection of the EAB presence in trees within urban areas and to ...
B. Hu, J. Li, J. Wang, B. Hall
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Peanut maturity classification using hyperspectral imagery
Biosystems Engineering, 2019 Seed maturity in peanut (Arachis hypogaea L.) determines economic return to a producer because of its impact on seed weight (yield), and critically influences seed vigor and other quality characteristics. During seed development, the inner mesocarp layer of the pericarp (hull) transitions in color from white to black as the seed matures.
Sheng Zou +5 more
openaire +3 more sources