Results 61 to 70 of about 57,062 (333)

Efficient Hyperspectral Image Classification Using Discrete Cosine Transform on Limited-Resource Systems

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Deep learning-based approaches to hyperspectral image analysis have attracted large attention and exhibited high performance in image classification tasks. However, deployment of deep learning-based hyperspectral image analysis systems is challenging due
Eungjoo Lee, Lei Pan, Zhu Li, Shuvra S. Bhattacharyya   +3 more
doaj   +1 more source

Excitonic Landscapes in Monolayer Lateral Heterostructures Revealed by Unsupervised Machine Learning

Advanced Optical Materials, EarlyView.
Hyperspectral photoluminescence data from graded MoxW1 − xS2 alloys and monolayer MoS2–WS2 lateral heterostructures are analyzed using unsupervised machine learning. The combined use of PCA, t‐SNE, and DBSCAN uncovers distinct excitonic regions that trace how composition, strain, and defects modulate optical responses in these 2D materials.
Maninder Kaur, Nicolas T. Sandino, Jason P. Terry, Mahdi Ghafariasl, Yohannes Abate   +4 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

Rapid Arbitrary‐Shape Microscopy of Unsectioned Tissues for Precise Intraoperative Tumor Margin Assessment

Advanced Science, EarlyView.
This study presents a novel microscopic imaging system capable of rapid, section‐free scanning of irregular tissue surfaces, delivering high sensitivity for detecting cancer cell clusters during intraoperative tumor margin assessment. Abstract Rapid and accurate intraoperative examination of tumor margins is crucial for precise surgical treatment, yet ...
Zhicheng Shao, Xiangwei Meng, Peifen Fu, Keer Huang, Xibei Chen, Yue Ying, Jun Li, Yuxin Zheng, Xiaolong Li, Hyeon Jeong Lee, Jingyi Feng, Yuexin Lu, Runping Cai, Xiongling Jiang, Qing Chen, Hong Zhang, Xiaoyong Man, Li Lin   +17 more
wiley   +1 more source

Synergistic 2D/3D Convolutional Neural Network for Hyperspectral Image Classification

Remote Sensing, 2020
Accurate hyperspectral image classification has been an important yet challenging task for years. With the recent success of deep learning in various tasks, 2-dimensional (2D)/3-dimensional (3D) convolutional neural networks (CNNs) have been exploited to
Xiaofei Yang, Xiaofeng Zhang, Yunming Ye, Raymond Y. K. Lau, Shijian Lu, Xutao Li, Xiaohui Huang   +6 more
doaj   +1 more source

Classification for hyperspectral imaging [PDF]

, 2014
Hyperspectral Imaging is a method of collecting and processing the information across pre-defined electromagnetic spectrum. These measurements make it possible to derive a continuous spectrum for each pixel of the image. After necessary adjustments these image spectra can be compared with database of reflectance spectra in order to recognise tested ...
Polak, Adam, Marshall, Stephen, Ren, Jinchang, Stothard, David J.M.   +3 more
openaire  

Hyperspectral Image Classification via Kernel Sparse Representation [PDF]

IEEE Transactions on Geoscience and Remote Sensing, 2011
In this paper, a new technique for hyperspectral image classification is proposed. Our approach relies on the sparse representation of a test sample with respect to all training samples in a feature space induced by a kernel function. Projecting the samples into the feature space and kernelizing the sparse representation improves the separability of ...
Yi Chen, Nasser M. Nasrabadi, Trac D. Tran   +2 more
openaire   +1 more source

Bayesian Gravitation-Based Classification for Hyperspectral Images

IEEE Transactions on Geoscience and Remote Sensing, 2022
Integration of spectral and spatial information is extremely important for the classification of high-resolution hyperspectral images (HSIs). Gravitation describes interaction among celestial bodies which can be applied to measure similarity between data for image classification.
Aizhu Zhang, Genyun Sun, Zhaojie Pan, Jinchang Ren, Xiuping Jia, Chenglong Zhang, Hang Fu, Yanjuan Yao   +7 more
openaire   +2 more sources

Real‐Time In Vivo Monitoring of Anastomotic Intestinal Ischemia Using Implantable Resorbable Organic Sensors

Advanced Science, EarlyView.
Resorbable impedance sensors are successfully implanted into porcine small intestinal anastomoses. Impedance was recorded for 2 hours prior, and 2 hours following ischemia induction, and a significant drop in tissue impedance was observed. Abstract Anastomotic failure remains one of the most severe complications in gastrointestinal surgery.
Dennis Wahl, Finn Jaekel, Julia Henne, Richard Kantelberg, Daniel C. Freund, Eberhard Grambow, Brigitte Vollmar, Amelie R. Zitzmann, Hans Kleemann, Jochen Hampe, Sebastian Hinz, Clemens Schafmayer, Karl Leo   +12 more
wiley   +1 more source