Results 51 to 60 of about 4,953 (252)

Hyperspectral Band Selection Using Improved Classification Map [PDF]

IEEE Geoscience and Remote Sensing Letters, 2017
Although it is a powerful feature selection algorithm, the wrapper method is rarely used for hyperspectral band selection. Its accuracy is restricted by the number of labeled training samples and collecting such label information for hyperspectral image is time consuming and expensive.
Xianghai Cao, Cuicui Wei, Jungong Han, Licheng Jiao   +3 more
openaire   +1 more source

Potassium‐Impurity‐Modulated Self‐Trapped Excitons and Energy Transfer in Phase‐Separated, Mn4+‐Doped Li2HfF6

Advanced Science, EarlyView.
Li2HfF6 contains two spectroscopically distinct self‐trapped excitons (STEs): intrinsic STE1 and redshifted, K‐associated STE2. Potassium incorporation stabilizes STE2, which selectively sensitizes Mn4+ red emission. This work uncovers the often‐overlooked role of trace K impurities, demonstrating impurity modulation as an effective strategy for tuning
Menuka Adhikari, Thomas Murray, Cameron J. Lewis, Jett Wu, Hari Adhikari, Cheng Li, Jianguo Wen, Hui Wu, Zhiping Luo   +8 more
wiley   +1 more source

Multi-objective particle swarm optimization with boundary selection and curve fitting for band selection in hyperspectral image classification

Journal of King Saud University: Computer and Information Sciences
Hyperspectral image (HSI) band selection often suffers from band redundancy and information loss due to the neglect of spectral characteristics. To address this issue, this paper proposes a semi-supervised multi-objective particle swarm optimization with
honghua rao, heming jia, changsheng wen
doaj   +1 more source

An Integrated Framework Coupling Hierarchical Hyperspectral i‐trait Screening, Gene Identification, and Functional Validation for Genetic Dissection of Salt Tolerance in Cotton

Advanced Science, EarlyView.
An integrated phenomics–genomics framework captures dynamic salt‐stress responses in laboratory‐grown cotton seedlings through time‐series hyperspectral imaging and non‐destructive i‐trait extraction. Hierarchical screening identifies biologically relevant, heritable, and non‐redundant traits for high‐resolution GWAS, identifying GhST7.
Bowei Xu, Le Liu, Mengli Yu, Yuling Guo, Junli Ye, Jun Zhang, Rumeng Zhao, Jiajie Yang, Wanneng Yang, Yameng Xu, Liqiang Fan, Zuoren Yang   +11 more
wiley   +1 more source

Unsupervised hyperspectral band selection by combination of unmixing and sequential clustering techniques

European Journal of Remote Sensing, 2019
Selecting the decisive spectral bands is a key issue in unsupervised hyperspectral band selection techniques. These methods are the most popular ways for dimensionality reduction of original data.
Sarra Ikram Benabadji, Moussa Sofiane Karoui, Khelifa Djerriri, Issam Boukerch, Nezha Farhi, Mohammed Amine Bouhlala   +5 more
doaj   +1 more source

Real‐Time Multicolor Fluorescence Microscopy via Cross‐Channel Acquisition and Deep‐Learning‐Based Inference

Advanced Intelligent Discovery, EarlyView.
Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto, Yuxin Jin, Nicholas Roy, Sarthak Misra   +3 more
wiley   +1 more source

Improving Soil Heavy Metal Lead Inversion Through Combined Band Selection Methods: A Case Study in Gejiu City, China

Sensors
Hyperspectral technology has become increasingly important in monitoring soil heavy metal pollution, yet hyperspectral data often contain substantial band redundancy, and band selection methods are typically limited to single algorithms or simple ...
Ping He, Xianfeng Cheng, Xingping Wen, Yi Cao, Yu Chen   +4 more
doaj   +1 more source

Band Selection Using Combined Divergence–Correlation Index and Sparse Loadings Representation for Hyperspectral Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Band selection and feature extraction are the two paradigms of hyperspectral dimensionality reduction. While feature extraction methods have many desirable properties, band selection methods keep the actual reflectances intact, which results in better ...
Munmun Baisantry, Anil Kumar Sao, Dericks Praise Shukla   +2 more
doaj   +1 more source

Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning

Advanced Intelligent Discovery, EarlyView.
Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang, Yongqing Zhang, Xinyu Deng, Hyeon Jeong Lee, Delong Zhang   +4 more
wiley   +1 more source

From Data to Discovery: Machine Learning–Enabled Intelligent Characterization of Two‐Dimensional Materials

Advanced Intelligent Discovery, EarlyView.
Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley   +1 more source