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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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Masked Graph Convolutional Network for Small Sample Classification of Hyperspectral Images
Remote Sensing, 2023 The deep learning method has achieved great success in hyperspectral image classification, but the lack of labeled training samples still restricts the development and application of deep learning methods.
Wenkai Liu +5 more
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A scalable dataflow accelerator for real time onboard hyperspectral image classification
, 2016 © Springer International Publishing Switzerland 2016.Real-time hyperspectral image classification is a necessary primitive in many remotely sensed image analysis applications.
C Gustavo +8 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
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Applied SciencesIn the field of hyperspectral image classification, deep learning technology, especially convolutional neural networks, has achieved remarkable progress.
Laiying Fu +3 more
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A Sparse Representation-Based Sample Pseudo-Labeling Method for Hyperspectral Image Classification
Remote Sensing, 2020 Hyperspectral image classification methods may not achieve good performance when a limited number of training samples are provided. However, labeling sufficient samples of hyperspectral images to achieve adequate training is quite expensive and difficult.
Binge Cui +4 more
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ISPRS International Journal of Geo-Information, 2021 Generation of a thematic map is important for scientists and agriculture engineers in analyzing different crops in a given field. Remote sensing data are well-accepted for image classification on a vast area of crop investigation.
Shiuan Wan, Mei-Ling Yeh, Hong-Lin Ma
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Programmable Spectrometry -- Per-pixel Classification of Materials using Learned Spectral Filters
, 2019 Many materials have distinct spectral profiles. This facilitates estimation of the material composition of a scene at each pixel by first acquiring its hyperspectral image, and subsequently filtering it using a bank of spectral profiles.
Sankaranarayanan, Aswin C. +1 more
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Advanced Materials, EarlyView.In this work, we discovered that optical gain within these polycrystalline perovskite thin films occurs primarily at defective sites, where, despite the low photoluminescence efficiency, we demonstrated high optical gain efficiency and long photocarrier lifetime. Our findings highlight the importance of defects in the development of electrically pumped
Chun‐Sheng Jack Wu +6 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingDeep 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 +3 more
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