Results 31 to 40 of about 75,553 (263)
Linear vs Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Image [PDF]
, 2017 As a new machine learning approach, extreme learning machine (ELM) has received wide attentions due to its good performances. However, when directly applied to the hyperspectral image (HSI) classification, the recognition rate is too low. This is because
Cao, Faxian +4 more
core +3 more sources
Smart Agricultural TechnologyThe accurate and effective monitoring of rice nitrogen status using hyperspectral datasets and estimation models is important for precision agriculture and intelligent breeding.
Zhonglin Wang +11 more
doaj +1 more source
PANSHARPENING OF HYPERSPECTRAL IMAGES IN URBAN AREAS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 Pansharpening has proven to be a valuable method for resolution enhancement of multi-band images when spatially high-resolving panchromatic images are available in addition. In principle, pansharpening can beneficially be applied to hyperspectral images
C. Chisense +3 more
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A New Vegetation Index in Short-Wave Infrared Region of Electromagnetic Spectrum
IEEE Access, 2021 Vegetation index algorithms based on radiance and/or reflectance data in the Visible Near Infrared (VNIR) band are created for multispectral and hyperspectral images to detect vegetation.
Yucel Cimtay +3 more
doaj +1 more source
Volume Holographic Hyperspectral Imaging [PDF]
Applied Optics, 2004 A volume hologram has two degenerate Bragg-phase-matching dimensions and provides the capability of volume holographic imaging. We demonstrate two volume holographic imaging architectures and investigate their imaging resolution, aberration, and sensitivity.
Wenhai, Liu +2 more
openaire +2 more sources
Robust Linear Spectral Unmixing using Anomaly Detection
, 2015 This paper presents a Bayesian algorithm for linear spectral unmixing of hyperspectral images that accounts for anomalies present in the data. The model proposed assumes that the pixel reflectances are linear mixtures of unknown endmembers, corrupted by ...
Altmann, Yoann +2 more
core +1 more source
Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral image [PDF]
, 2013 Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction
Benediktsson, Jon Atli +3 more
core +4 more sources
Open Physics, 2023 Spectral uncertainty is one of the most prominent spectral characteristics of hyperspectral images. Compared to the process of remote sensing hyperspectral imaging, hyperspectral imaging under land-based imaging conditions has the characteristics of ...
Zhao Jiale +6 more
doaj +1 more source
Hyperspectral Image Super-Resolution Algorithm Based on Graph Regular Tensor Ring Decomposition
Remote Sensing, 2023 This paper introduces a novel hyperspectral image super-resolution algorithm based on graph-regularized tensor ring decomposition aimed at resolving the challenges of hyperspectral image super-resolution.
Shasha Sun +5 more
doaj +1 more source
Quantitative Hyperspectral Reflectance Imaging [PDF]
Sensors, 2008 Hyperspectral imaging is a non-destructive optical analysis technique that can for instance be used to obtain information from cultural heritage objects unavailable with conventional colour or multi-spectral photography. This technique can be used to distinguish and recognize materials, to enhance the visibility of faint or obscured features, to detect
Ted A.G. Steemers +4 more
openaire +3 more sources