Results 91 to 100 of about 2,642,787 (294)

Enhancing hyperspectral image unmixing with spatial correlations [PDF]

, 2010
This paper describes a new algorithm for hyperspectral image unmixing. Most unmixing algorithms proposed in the literature do not take into account the possible spatial correlations between the pixels.
Jean-Yves Tourneret, Eches, Olivier, Olivier Eches, Nicolas Dobigeon, Dobigeon, Nicolas, Tourneret, Jean-Yves   +5 more
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

An End-to-End Framework for the Classification of Hyperspectral Images in the Wood Domain

IEEE Access
Hyperspectral images consist of a multitude of spectral bands for each pixel. Spectral bands provide information about wavelengths that may cover a larger spectrum of what the human eye may see.
Roberto Confalonieri, Phyu Phyu Htun, Boyuan Sun, Tammam Tillo   +3 more
doaj   +1 more source

Spatially Informed Feature Selection and Machine Learning in Matrix‐Assisted Laser Desorption/Ionization Imaging for Cohort‐Scale Molecular Tissue Phenomics in Glioblastoma

Advanced Intelligent Discovery, EarlyView.
Matrix‐assisted laser desorption/ionization imaging‐based identification of reliable small molecule markers across heterogeneous glioblastoma cohorts is challenging with intensity‐only methods. We present spatially informed feature selection (SIFS), a spatially informed framework that prioritizes molecules consistently colocalizing with histopathology.
Shad A. Mohammed, Denis Abu Sammour, Dirk C. Hoffmann, Philipp Sievers, Tobias Bausbacher, Melissa Hahn, Yasemin Ucal, Iris Mildenberger, Christiane A. Opitz, Stefan Schmidt, Michael Platten, Oliver Wasenmüller, Wolfgang Wick, Andreas von Deimling, Tobias Kessler, Carsten Hopf   +15 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

Bayesian estimation of linear mixtures using the normal compositional model. Application to hyperspectral imagery [PDF]

, 2010
This paper studies a new Bayesian unmixing algorithm for hyperspectral images. Each pixel of the image is modeled as a linear combination of so-called endmembers.
Eches, Olivier, Dobigeon, Nicolas, Tourneret, Jean-Yves, Mailhes, Corinne   +3 more
core   +1 more source

Robust Representation Learning for Clean Feature Discovery in Incomplete Multi‐View Clustering

Advanced Intelligent Systems, EarlyView.
Robust feature discovery in incomplete multi‐view clustering is achieved by coupling RPCA‐based clean representation recovery with neural‐network‐assisted graph learning. The resulting RIMVC framework constructs cleaner and more discriminative graph‐structured representations from incomplete and noisy multi‐view data, improving clustering robustness ...
Ping Hu, Chenggang Lu, Rui Zhang, Haijun Yan, Yesong Xu   +4 more
wiley   +1 more source

Proximal near-infrared hyperspectral imaging dataset for identifying epicuticular wax loss in Masena blueberries to evaluate post-harvest qualityDataverse

Data in Brief
The dataset presented in this paper consists of hyperspectral images of Masena blueberries that were harvested on November 24, 2023, from an orchard in Pukehina, New Zealand.
Shah Faisal, Yaminn Thawdar, Melanie Po-Leen Ooi, Peter Reutemann, Dale Fletcher, Ye Chow Kuang, Sanush K. Abeysekera   +6 more
doaj   +1 more source

Cross‐Sectoral AI Integration Is Essential to Tackling Food Waste and Food Insecurity: A Roadmap for Developing Resilient Food Systems

Advanced Intelligent Systems, EarlyView.
As food insecurity and global food demands surge, artificial intelligence (AI)‐based technologies offer promising opportunities to reduce food loss and waste. In this perspective, current AI adoption across the food supply chain is assessed using various academic, industry, and policy sources.
Akansha Prasad, Hannah Mann, Lubna Najm, Shadman Khan, Carlos D. M. Filipe, Tohid F. Didar   +5 more
wiley   +1 more source

An object-based approach to quantity and quality assessment of heathland habitats in the framework of natura 2000 using hyperspectral airborne ahs images [PDF]

, 2010
: Straightforward mapping of detailed heathland habitat patches and their quality using remote sensing is hampered by (1) the intrinsic property of a high heterogeneity in habitat species composition (i.e.
Spanhove, T., Scheunders, Paul, Scheunders, P., Haest, B., Thoonen, G., Bertels, L., Kooistra, L., Thoonen, Guy, Vanden Borre, J., Mücher, C.A., Delalieux, S., Mucher, C. A.   +11 more
core