Results 81 to 90 of about 3,420,393 (309)

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

Advancing Fruit Bioimpedance Monitoring With Sustainable, Soft, And Bio‐Based Electrodes Beyond ECG

Advanced Electronic Materials, EarlyView.
Electrical impedance spectroscopy enables non‐destructive fruit quality monitoring, but conventional ECG and needle electrodes compromise signal stability, fruit physiology, and sustainability. This perspective highlights the transition toward soft, biocompatible, and biodegradable electrode interfaces based on natural substrates, bio‐derived ...
Sundus Riaz, Ahmed Rasheed, Angelo Zanella, Almudena Rivadeneyra, Paolo Lugli, Luisa Petti, Pietro Ibba   +6 more
wiley   +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

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 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

Unsupervised Band Selection Based on Evolutionary Multiobjective Optimization for Hyperspectral Images

, 2016
Band selection is an important preprocessing step for hyperspectral image processing. Many valid criteria have been proposed for band selection, and these criteria model band selection as a single-objective optimization problem.
Yuan, Yuan, Gong, Maoguo, Zhang, Mingyang   +2 more
core   +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

Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking

, 2016
Saliency detection has been a hot topic in recent years, and many efforts have been devoted in this area. Unfortunately, the results of saliency detection can hardly be utilized in general applications.
Lin, Jianzhe, Yuan, Yuan, Wang, Qi
core   +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

Infinite Latent Feature Selection Technique for Hyperspectral Image Classification

Jurnal Elektronika dan Telekomunikasi, 2019
The classification process is one of the most crucial processes in hyperspectral imaging. One of the limitations in classification process using machine learning technique is its complexities, where hyperspectral image format has a thousand band that can
Tajul Miftahushudur, Chaeriah Bin Ali Wael, Teguh Praludi   +2 more
doaj   +1 more source