Results 31 to 40 of about 2,902 (152)

Semi-Supervised Classification for Hyperspectral Images Based on Multiple Classifiers and Relaxation Strategy

ISPRS International Journal of Geo-Information, 2018
Hyperspectral image (HSI) classification is a fundamental and challenging problem in remote sensing and its various applications. However, it is difficult to perfectly classify remotely sensed hyperspectral data by directly using classification ...
Fuding Xie, Dongcui Hu, Fangfei Li, Jun Yang, Deshan Liu   +4 more
doaj   +1 more source

Spectral Segmentation Multi-Scale Feature Extraction Residual Networks for Hyperspectral Image Classification

Remote Sensing, 2023
Hyperspectral image (HSI) classification is a vital task in hyperspectral image processing and applications. Convolutional neural networks (CNN) are becoming an effective approach for categorizing hyperspectral remote sensing images as deep learning ...
Jiamei Wang, Jiansi Ren, Yinbin Peng, Meilin Shi   +3 more
doaj   +1 more source

Hyperspectral image classification on insufficient-sample and feature learning using deep neural networks: A review

International Journal of Applied Earth Observations and Geoinformation, 2021
Over the years, advances in sensor technologies have enhanced spatial, temporal, spectral, and radiometric resolutions, thus significantly improving the size, resolution, and quality of imagery.
Naftaly Wambugu, Yiping Chen, Zhenlong Xiao, Kun Tan, Mingqiang Wei, Xiaoxue Liu, Jonathan Li   +6 more
doaj   +1 more source

mHC-HSI: Clustering-Guided Hyper-Connection Mamba for Hyperspectral Image Classification

CoRR
Recently, DeepSeek has invented the manifold-constrained hyper-connection (mHC) approach which has demonstrated significant improvements over the traditional residual connection in deep learning models \cite{xie2026mhc}. Nevertheless, this approach has not been tailor-designed for improving hyperspectral image (HSI) classification.
Yimin Zhu 0002, Zack Dewis, Quinn Ledingham, Saeid Taleghanidoozdoozan, Mabel Heffring, Zhengsen Xu, Motasem Alkayid, Megan Greenwood, Lincoln Linlin Xu   +8 more
openaire   +2 more sources

What Are Computer‐Assisted Methods Achieving in Fine‐Needle Aspiration Cytology of the Pancreas? A Systematic Review and Meta‐Analysis

Cytopathology, EarlyView.
This meta‐analysis demonstrates high diagnostic accuracy of computer‐assisted methods in pancreatic EUS‐FNA cytology (AUC 0.92–0.96) and supports an integrated, cytopathologist‐led workflow in which artificial intelligence functions as an adjunct to diagnostic interpretation and on‐site evaluation (ROSE). Created in BioRender. Mohamed Mirzan, A. (2026)
Al‐Amaan Mohamed Mirzan, Roberto Dina
wiley   +1 more source

Spectral Swin Transformer Network for Hyperspectral Image Classification

Remote Sensing, 2023
Hyperspectral images are complex images that contain more spectral dimension information than ordinary images. An increasing number of HSI classification methods are using deep learning techniques to process three-dimensional data. The Vision Transformer
Baisen Liu, Yuanjia Liu, Wulin Zhang, Yiran Tian, Weili Kong   +4 more
doaj   +1 more source

Brain tissue classification in hyperspectral images using multistage diffusion features and transformer

Journal of Microscopy, EarlyView.
Abstract Brain surgery is a widely practised and effective treatment for brain tumours, but accurately identifying and classifying tumour boundaries is crucial to maximise resection and avoid neurological complications. This precision in classification is essential for guiding surgical decisions and subsequent treatment planning.
Neetu Sigger, Tuan T. Nguyen, Gianluca Tozzi   +2 more
wiley   +1 more source

A Hypered Deep-Learning-Based Model of Hyperspectral Images Generation and Classification for Imbalanced Data

Remote Sensing, 2022
Recently, hyperspectral image (HSI) classification has become a hot topic in the geographical images research area. Sufficient samples are required for image classes to properly train classification models.
Hasan A. H. Naji, Tianfeng Li, Qingji Xue, Xindong Duan   +3 more
doaj   +1 more source

Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection.
Xu Wang, Daeun Choi, Damian Adams, Jaehyun Ahn, Kelly Balmant, Raquel Dias, Carlos D. Messina, Rafael Muñoz‐Carpena, Vance M. Whitaker, Haipeng Yu, Ziwen Yu, Chang Zhao, Changying Li   +12 more
wiley   +1 more source

L‐Probe: New Approach for Spike Removal in Raman Hyperspectral Imaging Based on Local Prominence Peak Filtering

Journal of Chemometrics, Volume 40, Issue 8, August 2026.
ABSTRACT Raman hyperspectral imaging is an indispensable analytical technique that can provide simultaneous chemical and spatial information of a sample, enabling a higher comprehension and sampling of the analyzed material. However, a known challenge in processing large hyperspectral Raman datasets is the effective removal of spurious, high‐intensity ...
Leonardo Francisco Rafael Lemes, Victor Klassen de Oliveira, Frederico Luis Felipe Soares   +2 more
wiley   +1 more source