Results 61 to 70 of about 13,575,612 (179)

An Adaptive Support Vector Machine Optimized by an Improved Starfish Optimization Algorithm for Hyperspectral Image Classification

Biomimetics
This study proposes an adaptive Support Vector Machine (SVM) classification method based on an enhanced Starfish Optimization Algorithm (SFOAE-SVM) for hyperspectral image (HSI) classification.
Yi Zhang, Changyi Feng, Yong Xu
doaj   +1 more source

Non‐Destructive Analysis of Phenolic and Flavonoid Contents in Medicinal Plant Powders Using Hyperspectral Imaging and Variable Selection

Analytical Science Advances, Volume 7, Issue 2, December 2026.
ABSTRACT Phenolic and flavonoid contents in medicinal plants are essential to their growth and development and provide numerous health benefits, yet their quantification using traditional wet chemistry is labor‐intensive and time‐consuming. This study utilized the combination of two benchtop hyperspectral imaging (HSI) systems, namely short‐wave ...
Rahul Joshi, Sushma Kholiya, Himanshu Pandey, Mahipal Singh, Ameeta Tiwari, Jinsu Lim, Ramaraj Sathasivam, Mohammad Akbar Faqeerzada, Sang Un Park, Byoung‐Kwan Cho   +9 more
wiley   +1 more source

FAHM: Frequency-Aware Hierarchical Mamba for Hyperspectral Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Convolutional neural networks and Transformers have garnered substantial attention in hyperspectral image (HSI) classification, and recently Mamba has made significant progress in modeling long sequences.
Pei-Xian Zhuang, Xiao-Chen Zhang, Hao Wang, Tian-Xiang Zhang, Leiming Liu, Jiang-Yun Li   +5 more
semanticscholar   +1 more source

3DSS-Mamba: 3D-Spectral-Spatial Mamba for Hyperspectral Image Classification [PDF]

IEEE Transactions on Geoscience and Remote Sensing
Hyperspectral image (HSI) classification constitutes the fundamental research in remote sensing fields. Convolutional neural networks (CNNs) and Transformers have demonstrated impressive capability in capturing spectral-spatial contextual dependencies ...
Yan He, Bing Tu, Bo Liu, Jun Li, A. Plaza   +4 more
semanticscholar   +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

Semantic feature extraction method for hyperspectral crop classification [PDF]

, 2021
Hyperspectral imaging (HSI) is composed of several hundred of narrow bands (NB) with high spectral correlation and is widely used in crop classification; thus, induces time and space complexity, resulting in high computational overhead and Hughes ...
Babu, M. C. Girish, C., Padma M., Girish Baabu, M. C., Padma M. C., M. C. Girish Baabu   +4 more
core   +1 more source

Multi-Scale CNN Based Garbage Detection of Airborne Hyperspectral Data

IEEE Access, 2019
Garbage detection is important for environmental monitoring in large areas. However, the manual patrol is time-consuming and labor-intensive. This paper proposes a method for monitoring garbage distribution in large areas with airborne hyperspectral data.
Dan Zeng, Shun Zhang, Fansheng Chen, Yueming Wang   +3 more
doaj   +1 more source

DualMamba: A Lightweight Spectral–Spatial Mamba-Convolution Network for Hyperspectral Image Classification [PDF]

IEEE Transactions on Geoscience and Remote Sensing
The effectiveness and efficiency of modeling complex spectral–spatial relations are crucial for hyperspectral image (HSI) classification. Most existing methods based on convolution neural networks (CNNs) and transformers still suffer from heavy ...
Jiamu Sheng, Jingyi Zhou, Jiong Wang, Peng Ye, Jiayuan Fan   +4 more
semanticscholar   +1 more source

Determination of dry bean physiological maturity from UAS RGB time series using a stacking ensemble of color‐derived spectral features

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Evaluating physiological maturity is an important trait in dry bean breeding (Phaseolus vulgaris L.), but field scoring can be inaccurate, subjective, and a persistent bottleneck in multi‐environment trials. To address this, we developed a low‐cost, high‐throughput pipeline that predicts plot‐level days after planting at maturity from time ...
Aliasghar Bazrafkan, Lovepreet Singh, John Hawkins, Evan M. Wright, Valerio Hoyos‐Villegas   +4 more
wiley   +1 more source

Triplet Constrained Deep Feature Extraction for Hyperspectral Image Classification

, 2018
Convolutional neural networks (CNNs) have demonstrated significant performance in various visual recognition problems in recent years. Recent research has shown that training multilayer neural networks can extensively improve the performance of ...
Zhou, Jun, Alam, Fahim, Jo, Jun, Liew, Wee Chung, Gao, Yongsheng, Fahim Irfan Alam, Jun Zhou, Alan Wee-Chung Liew, Jun Jo, Yongsheng Gao   +9 more
core   +1 more source