Results 31 to 40 of about 9,650 (249)
Remote Sensing, 2019 In recent decades, in order to enhance the performance of hyperspectral image classification, the spatial information of hyperspectral image obtained by various methods has become a research hotspot. For this work, it proposes a new classification method
Jianshang Liao, Liguo Wang
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Advances in hyperspectral image classification
IEEE SIGNAL PROCESSING MAGAZINE, 2014 The technological evolution of optical sensors over the last few decades has provided remote sensing analysts with rich spatial, spectral, and temporal information. In particular, the increase in spectral resolution of hyperspectral images (HSIs) and infrared sounders opens the doors to new application domains and poses new methodological challenges in
Camps-Valls, Gustavo +3 more
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A DIVERSIFIED DEEP BELIEF NETWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 In recent years, researches in remote sensing demonstrated that deep architectures with multiple layers can potentially extract abstract and invariant features for better hyperspectral image classification. Since the usual real-world hyperspectral image
P. Zhong, Z. Q. Gong, C. Schönlieb
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Capsule Networks for Hyperspectral Image Classification [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2019 Convolutional neural networks (CNNs) have recently exhibited an excellent performance in hyperspectral image classification tasks. However, the straightforward CNN-based network architecture still finds obstacles when effectively exploiting the relationships between hyperspectral imaging (HSI) features in the spectral–spatial domain, which is a key ...
Mercedes Eugenia Paoletti +6 more
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Advanced Materials, EarlyView.AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll +19 more
wiley +1 more source
Deep Learning Enables Identification of Antimicrobial Peptides Through Mechanochromic Fingerprints
Angewandte Chemie, EarlyView.We demonstrate a new platform for antimicrobial peptide identification by combining polydiacetylene, hyperspectral imaging, and deep learning. The trained model classifies distinct spectral fingerprints into 14 peptide classes with 96.79% accuracy, revealing previously hidden molecular information beyond conventional colorimetric sensing.
Jiali Chen +4 more
wiley +2 more sources
Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism
Remote Sensing, 2021 In recent years, image classification on hyperspectral imagery utilizing deep learning algorithms has attained good results. Thus, spurred by that finding and to further improve the deep learning classification accuracy, we propose a multi-scale residual
Yuhao Qing, Wenyi Liu
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SYNERGETICS FRAMEWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013 Abstract. In this paper a new classification technique for hyperspectral data based on synergetics theory is presented. Synergetics – originally introduced by the physicist H. Haken – is an interdisciplinary theory to find general rules for pattern formation through selforganization and has been successfully applied in fields ranging from biology to ...
Müller, Rupert +2 more
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Advanced Science, EarlyView.Thermally activated LiHCOO induces in situ pseudo‐halide diffusion, promoting buried interface strain release and perovskite crystallization. The monoclinic LiHCOO phase forms an open framework structure that enhances HCOO− diffusion and drives interfacial restructuring.
Chao Gao +4 more
wiley +1 more source
EVALUATING THE INITIALIZATION METHODS OF WAVELET NETWORKS FOR HYPERSPECTRAL IMAGE CLASSIFICATION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 The idea of using artificial neural network has been proven useful for hyperspectral image classification. However, the high dimensionality of hyperspectral images usually leads to the failure of constructing an effective neural network classifier.
P.-H. Hsu
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