Improved Transformer Net for Hyperspectral Image Classification
Remote Sensing, 2021 In recent years, deep learning has been successfully applied to hyperspectral image classification (HSI) problems, with several convolutional neural network (CNN) based models achieving an appealing classification performance.
Yuhao Qing +3 more
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An Adaptive Support Vector Machine Optimized by an Improved Starfish Optimization Algorithm for Hyperspectral Image Classification [PDF]
BiomimeticsThis study proposes an adaptive Support Vector Machine (SVM) classification method based on an enhanced Starfish Optimization Algorithm (SFOAE-SVM) for hyperspectral image (HSI) classification.
Yi Zhang, Changyi Feng, Yong Xu
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Detection of Frost-Damaged Maize Kernels for Food Quality Screening Using Hyperspectral Imaging and 2D-COS-Assisted Deep Learning. [PDF]
Food Sci NutrThis work presents a nondestructive strategy for identifying frost‐damaged maize kernels using hyperspectral imaging (900–1700 nm) combined with 2D‐COS‐assisted deep learning. MSC preprocessing was adopted, and 432 frost‐damage‐related characteristic bands were extracted by 2D‐COS.
Nan J +7 more
europepmc +2 more sources
Non-Destructive Analysis of Phenolic and Flavonoid Contents in Medicinal Plant Powders Using Hyperspectral Imaging and Variable Selection. [PDF]
Anal Sci AdvABSTRACT Phenolic and flavonoid contents in medicinal plants are essential to their growth and development and provide numerous health benefits, yet their quantification using traditional wet chemistry is labor‐intensive and time‐consuming. This study utilized the combination of two benchtop hyperspectral imaging (HSI) systems, namely short‐wave ...
Joshi R +9 more
europepmc +2 more sources
Linear Variable Filter-Based Wide-Field Hyperspectral Camera for Multiplex Immunoplasmonics Imaging. [PDF]
J BiophotonicsA compact LVTF‐based wide‐field hyperspectral camera integrated with reflected light microscopy enables rapid, high‐resolution imaging of plasmonic nanoparticles on cells. The system improves spatial resolution, signal‐to‐noise ratio, and acquisition speed over push‐broom methods, achieving accurate multiplex nanoparticle classification (97% F‐score ...
Darviot C, Meunier M.
europepmc +2 more sources
SOFT COMPUTING APPROACHES FOR HYPERSPECTRAL IMAGE CLASSIFICATION [PDF]
ICTACT Journal on Soft Computing, 2020 Hyperspectral image classification is one of the most emerging form of image classification. It is able to convey information about an image in a more detailed way as compared to RGB or multispectral data.
H S Prasantha +3 more
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IEEE Access, 2021 The identification of tree species is of great significance to the sustainable management and utilization of forest ecosystems. Hyperspectral data provide sufficient spectral and spatial information to classify tree species. Convolutional neural networks
Yun Shi, Donghui Ma, Jie Lv, Jie Li
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Contrastive Learning Based on Transformer for Hyperspectral Image Classification
Applied Sciences, 2021 Recently, deep learning has achieved breakthroughs in hyperspectral image (HSI) classification. Deep-learning-based classifiers require a large number of labeled samples for training to provide excellent performance.
Xiang Hu +4 more
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SquconvNet: Deep Sequencer Convolutional Network for Hyperspectral Image Classification
Remote Sensing, 2023 The application of Transformer in computer vision has had the most significant influence of all the deep learning developments over the past five years.
Bing Li +4 more
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WHU-OHS: A benchmark dataset for large-scale Hersepctral Image classification
International Journal of Applied Earth Observations and Geoinformation, 2022 Hyperspectral image (HSI) classification is one of the most important remote sensing techniques. Currently, the performances of most of the HSI classification networks on the public HSI datasets are overoptimistic (i.e., the overall accuracy exceeds 98 %)
Jiayi Li, Xin Huang, Lilin Tu
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