Results 81 to 90 of about 57,062 (333)

Macrophage Phenotype Detection Methodology on Textured Surfaces via Nuclear Morphology Using Machine Learning

Advanced Intelligent Discovery, EarlyView.
A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi, Kateryna Andriienkova, Tetyana Goldanova, Ezgi Antmen, Thibaut Soubrie, Nihal Engin Vrana   +5 more
wiley   +1 more source

A Robust Rule-Based Ensemble Framework Using Mean-Shift Segmentation for Hyperspectral Image Classification

Remote Sensing, 2019
This paper assesses the performance of DoTRules—a dictionary of trusted rules—as a supervised rule-based ensemble framework based on the mean-shift segmentation for hyperspectral image classification.
Majid Shadman Roodposhti, Arko Lucieer, Asim Anees, Brett A. Bryan   +3 more
doaj   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Graph inductive learning method for small sample classification of hyperspectral remote sensing images

European Journal of Remote Sensing, 2020
In recent years, deep learning has drawn increasing attention in the field of hyperspectral remote sensing image classification and has achieved great success.
Xibing Zuo, Xuchu Yu, Bing Liu, Pengqiang Zhang, Xiong Tan, Xiangpo Wei   +5 more
doaj   +1 more source

A Comparison of AVIRIS and Landsat for Land Use Classification at the Urban Fringe

, 2004
In this study we tested whether AVIRIS data allowed for improved land use classification over synthetic Landsat ETM+ data for a location on the urban-rural fringe of Colorado.
Goetz, Alexander F.H., Platt, Rutherford V.   +1 more
core   +1 more source

Physically Based Predictive Modelling of Archaeological Proxies Using Cropmarks

Archaeological Prospection, EarlyView.
ABSTRACT Cropmarks, as archaeological proxies, offer a valuable means of detecting buried sites through remote sensing. Yet, the scalability of such methods across varied archaeological contexts remains underexplored, and AI‐based modelling approaches are still in early stages.
Elias Gravanis, Athos Agapiou
wiley   +1 more source

SSATNet: Spectral-spatial attention transformer for hyperspectral corn image classification

Frontiers in Plant Science
Hyperspectral images are rich in spectral and spatial information, providing a detailed and comprehensive description of objects, which makes hyperspectral image analysis technology essential in intelligent agriculture.
Bin Wang, Gongchao Chen, Juan Wen, Linfang Li, Songlin Jin, Yan Li, Ling Zhou, Weidong Zhang   +7 more
doaj   +1 more source

Archaeological Damage Assessment in Conflict Zones: Integrating Satellite Imagery and Ground Surveys in Daraa, Syria

Archaeological Prospection, EarlyView.
ABSTRACT Satellite remote sensing is among the most significant modern methodologies supporting field archaeology. In addition to its efficiency in identifying archaeological sites, remote sensing offers a safe and cost‐effective approach in conflict zones.
Amal Al Kassem, Kristen Hopper, Ismael M. Ibraheem, Andreas Maier, Mohammad Khair Nassrallah, Jürgen Richter   +5 more
wiley   +1 more source

A novel hyperspectral image classification approach based on multiresolution segmentation with a few labeled samples

International Journal of Advanced Robotic Systems, 2017
Hyperspectral remote sensing technology becomes more and more popular in recent years which can be applied to satellite, plane, and flying robots. An important application of hyperspectral remote sensing is the classification of ground objects.
Binge Cui, Xiudan Ma, Faxi Zhao, Yanan Wu   +3 more
doaj   +1 more source

A Spectral-Texture Kernel-Based Classification Method for Hyperspectral Images

Remote Sensing, 2016
Classification of hyperspectral images always suffers from high dimensionality and very limited labeled samples. Recently, the spectral-spatial classification has attracted considerable attention and can achieve higher classification accuracy and ...
Yi Wang, Yan Zhang, Haiwei Song
doaj   +1 more source