Results 11 to 20 of about 30,598 (209)
Hyperspectral and Multispectral Image Fusion by Deep Neural Network in a Self-Supervised Manner
Remote Sensing, 2021 Compared with multispectral sensors, hyperspectral sensors obtain images with high- spectral resolution at the cost of spatial resolution, which constrains the further and precise application of hyperspectral images.
Jianhao Gao, Jie Li, Menghui Jiang
doaj +1 more source
SICE Journal of Control, Measurement, and System Integration, 2022 Recently, the utilization of hyperspectral images containing several hundred wavelength information has been increasing in various fields. If a hyperspectral image can be estimated from a low-cost RGB image that has only R, G, and B wavelength ...
Ryoji Sato +3 more
doaj +1 more source
Deep Learning in Medical Hyperspectral Images: A Review
Sensors, 2022 With the continuous progress of development, deep learning has made good progress in the analysis and recognition of images, which has also triggered some researchers to explore the area of combining deep learning with hyperspectral medical images and ...
Rong Cui +6 more
doaj +1 more source
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
doaj +1 more source
Nonlinear unmixing of hyperspectral images: Models and algorithms [PDF]
, 2013 When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM).
McLaughlin, Stephen; id_orcid +14 more
core +1 more source
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
doaj +1 more source
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
doaj +1 more source
The Future of Hyperspectral Imaging [PDF]
Journal of Imaging, 2019 The Special Issue on hyperspectral imaging (HSI), entitled “The Future of Hyperspectral Imaging”, has published 12 papers. Nine papers are related to specific current research and three more are review contributions: In both cases, the request is to propose those methods or instruments so as to show the future trends of HSI.
openaire +6 more sources
Nonlinear spectral unmixing of hyperspectral images using Gaussian processes [PDF]
, 2012 This paper presents an unsupervised algorithm for nonlinear unmixing of hyperspectral images. The proposed model assumes that the pixel reflectances result from a nonlinear function of the abundance vectors associated with the pure spectral components ...
Altmann, Yoann +5 more
core +1 more source
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
doaj +1 more source