Results 51 to 60 of about 12,889,375 (285)
Hyperspectral colon tissue cell classification [PDF]
, 2004 A novel algorithm to discriminate between normal and malignant tissue cells of the human colon is presented. The microscopic level images of human colon tissue cells were acquired using hyperspectral imaging technology at contiguous wavelength intervals ...
Rajpoot, Nasir M. (Nasir Mahmood) +2 more
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Advanced Science, EarlyView.Super‐multiplexed Label‐free Raman Imaging (SLRI) enables 2D/3D metabolic mapping of intact Drosophila testes. Moving beyond descriptive morphology, it establishes a multidimensional tool for tissue metabolic remodeling, and offers a generalizable platform for complex tissue analysis, with implications extending to development and disease. ABSTRACT The
Jiaxin Li +23 more
wiley +1 more source
Applied SciencesIn the field of hyperspectral image classification, deep learning technology, especially convolutional neural networks, has achieved remarkable progress.
Laiying Fu +3 more
doaj +1 more source
ISPRS International Journal of Geo-Information, 2021 Generation of a thematic map is important for scientists and agriculture engineers in analyzing different crops in a given field. Remote sensing data are well-accepted for image classification on a vast area of crop investigation.
Shiuan Wan, Mei-Ling Yeh, Hong-Lin Ma
doaj +1 more source
CoSP: Reconfigurable Metamaterial Inverse Design via Contrastive Pretrained Large Language Model
Advanced Science, EarlyView.In this work, CoSP (contrastive multi‐state pretrain), an intelligent inverse design method for reconfigurable metamaterials based on a contrastive pretrained large language model, is proposed. Numerical experiments demonstrate that CoSP can design reconfigurable metamaterial structures for multi‐state, multi‐band optical responses, showing great ...
Shujie Yang +4 more
wiley +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingDeep learning-based approaches to hyperspectral image analysis have attracted large attention and exhibited high performance in image classification tasks. However, deployment of deep learning-based hyperspectral image analysis systems is challenging due
Eungjoo Lee +3 more
doaj +1 more source
A Sparse Representation-Based Sample Pseudo-Labeling Method for Hyperspectral Image Classification
Remote Sensing, 2020 Hyperspectral image classification methods may not achieve good performance when a limited number of training samples are provided. However, labeling sufficient samples of hyperspectral images to achieve adequate training is quite expensive and difficult.
Binge Cui +4 more
doaj +1 more source
Masked Graph Convolutional Network for Small Sample Classification of Hyperspectral Images
Remote Sensing, 2023 The deep learning method has achieved great success in hyperspectral image classification, but the lack of labeled training samples still restricts the development and application of deep learning methods.
Wenkai Liu +5 more
doaj +1 more source
Hyperspectral Image Classification [PDF]
, 2012 One objective of hyperspectral data processing is to classify collected imagery into distinct material constituents relevant to particular applications, and produce classification maps that indicate where the constituents are present. Such information products can include land-cover maps for environmental remote sensing, surface mineral maps for ...
openaire +1 more source
Advanced Science, EarlyView.A proof‐of‐concept multifunctional capsule robot integrates diagnostic imaging, magnetic navigation, wireless power transfer, and targeted thermotherapy for gastrointestinal applications. Magnetically guided positioning and stable wireless power enable localized thermal ablation, achieving controlled heating and high cell mortality in vitro.
Heng Zhang +7 more
wiley +1 more source