Results 11 to 20 of about 75,553 (263)
Agricultural plant hyperspectral imaging dataset
Компьютерная оптика, 2023 Detailed automated analysis of crop images is critical to the development of smart agriculture and can significantly improve the quantity and quality of agricultural products.
A.V. Gaidel +6 more
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Hyperspectral Imaging for Bloodstain Identification [PDF]
Sensors, 2021 Blood is key evidence to reconstruct crime scenes in forensic sciences. Blood identification can help to confirm a suspect, and for that reason, several chemical methods are used to reconstruct the crime scene however, these methods can affect subsequent DNA analysis. Therefore, this study presents a non-destructive method for bloodstain identification
Maheen Zulfiqar +4 more
openaire +3 more sources
FUSION OF HYPERSPECTRAL AND PANCHROMATIC IMAGES USING SPECTRAL UNMIXING RESULTS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013 Hyperspectral imaging, due to providing high spectral resolution images, is one of the most important tools in the remote sensing field. Because of technological restrictions hyperspectral sensors has a limited spatial resolution.
R. Rajabi, H. Ghassemian
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Tongue Coating Grading Identification Using Deep Learning for Hyperspectral Imaging Data
IEEE Access, 2023 Tongue diagnosis is one of the four diagnostic methods of traditional Chinese medicine (TCM), which has important value in clinical disease diagnosis and efficacy evaluation.
Dong Zhang +4 more
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Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods [PDF]
, 2013 Hyperspectral images show similar statistical properties to natural grayscale or color photographic images. However, the classification of hyperspectral images is more challenging because of the very high dimensionality of the pixels and the small number
Benediktsson, Jón Atli +3 more
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Deep Pansharpening via 3D Spectral Super-Resolution Network and Discrepancy-Based Gradient Transfer
Remote Sensing, 2022 High-resolution (HR) multispectral (MS) images contain sharper detail and structure compared to the ground truth high-resolution hyperspectral (HS) images. In this paper, we propose a novel supervised learning method, which considers pansharpening as the
Haonan Su, Haiyan Jin, Ce Sun
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HIDSAG: Hyperspectral Image Database for Supervised Analysis in Geometallurgy
Scientific Data, 2023 Supervised analysis using spectral data requires a well-informed characterisation of the response variables and abundant spectral data points. The presented hyperspectral dataset comes from five sets of geometallurgical samples, each characterised by ...
Alejandro Ehrenfeld +6 more
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Multi-temporal Hyperspectral Anomaly Change Detection Based on Dual Space Conjugate Autoencoder [PDF]
Jisuanji kexue, 2023 Hyperspectral anomaly change detection can find anomaly changes from multi-temporal hyperspectral remote sensing images.These anomaly changes are rare,different from the overall background change trend,difficult to be found,but very intere-sting.For the ...
LI Shasha, XING Hongjie, LI Gang
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Quality criteria benchmark for hyperspectral imagery [PDF]
, 2005 Hyperspectral data appear to be of a growing interest over the past few years. However, applications for hyperspectral data are still in their infancy as handling the significant size of the data presents a challenge for the user community. Efficient
Christophe, Emmanuel +2 more
core +3 more sources
Remote Sensing Performance Enhancement in Hyperspectral Images
Sensors, 2018 Hyperspectral images with hundreds of spectral bands have been proven to yield high performance in material classification. However, despite intensive advancement in hardware, the spatial resolution is still somewhat low, as compared to that of color and
Chiman Kwan
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