Results 61 to 70 of about 12,889,375 (285)

Is Pretraining Necessary for hyperspectral image classification? [PDF]

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019
We address two questions for training a convolutional neural network (CNN) for hyperspectral image classification: i) is it possible to build a pre-trained network? and ii) is the pre-training effective in furthering the performance? To answer the first question, we have devised an approach that pre-trains a network on multiple source datasets that ...
Hyungtae Lee, Sungmin Eum, Heesung Kwon
openaire   +3 more sources

Seeing the Chemistry of Biomolecular Condensates: In Situ Mapping of Composition and Water Content

Advanced Science, EarlyView.
Raman hyperspectral imaging combined with spectral phasor analysis enables a label‐free quantification of proteins, polymers and water density within individual biomolecular condensates. By transforming complex vibrational fingerprints into intuitive compositional maps, the approach reveals the high water content and structural heterogeneity of ...
E. Sabri, A. Mangiarotti, C. N. Z. Schmitt, R. Dimova   +3 more
wiley   +1 more source

Fast Spectral Clustering for Unsupervised Hyperspectral Image Classification

Remote Sensing, 2019
Hyperspectral image classification is a challenging and significant domain in the field of remote sensing with numerous applications in agriculture, environmental science, mineralogy, and surveillance.
Yang Zhao, Yuan Yuan, Qi Wang
doaj   +1 more source

A Spectral Spatial Attention Fusion with Deformable Convolutional Residual Network for Hyperspectral Image Classification

Remote Sensing, 2021
Convolutional neural networks (CNNs) have exhibited excellent performance in hyperspectral image classification. However, due to the lack of labeled hyperspectral data, it is difficult to achieve high classification accuracy of hyperspectral images with ...
Tianyu Zhang, Cuiping Shi, Diling Liao, Liguo Wang   +3 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

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

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

Multiple Feature Learning Based on Edge-Preserving Features for Hyperspectral Image Classification

IEEE Access, 2019
The classification of hyperspectral images is the basis and hotspot in the research of hyperspectral images. In this paper, a classification algorithm of hyperspectral image based on multiple edge-preserving features and multiple feature learning (MFL ...
Wei Tian, Lizhong Xu, Zhe Chen, Aiye Shi
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

High Dimensional Feature for Hyperspectral Image Classification

MATEC Web of Conferences, 2018
Making a high dimensional (e.g., 100k-dim) feature for hyperspectral image classification seems not a good idea because it will bring difficulties on consequent training, computation, and storage.
Wang Cailing, Wang Hongwei, Zhang Yinyong, Wen Jia, Yang Fan   +4 more
doaj   +1 more source