Results 11 to 20 of about 7,130 (165)
Hyperspectral Imaging Reveals High Water and Hemoglobin Content at Rest and Decreased Oxygen Levels After Physical Activity at the Residual Limb of Non-Dysvascular Lower Limb Amputees. [PDF]
MicrocirculationABSTRACT Objective Skin integrity is one factor determining residual limb health. Oxygen deficiency caused by energy consumption and/or mechanical stress is the most common reason for skin breakdown at the residual limb (RL), limiting physical activity and causing residual limb pain (RLP).
Pardo LA +6 more
europepmc +2 more sources
Segment-Based Clustering of Hyperspectral Images Using Tree-Based Data Partitioning Structures
Algorithms, 2020 Hyperspectral image classification has been increasingly used in the field of remote sensing. In this study, a new clustering framework for large-scale hyperspectral image (HSI) classification is proposed.
Mohamed Ismail, Milica Orlandić
doaj +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Convolutional neural networks (CNN) have achieved excellent performance for the hyperspectral image (HSI) classification problem due to better extracting spectral and spatial information.
Pan Yang +5 more
doaj +1 more source
HYPERSPECTRAL IMAGE CLASSIFICATION USING MULTI-LAYER PERCEPTRON MIXER (MLP-MIXER) [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023 The classifying of hyperspectral images (HSI) is a difficult task given the high dimensionality of the space, the huge number of spectral bands, and the small number of labeled data.
A. Jamali +3 more
doaj +1 more source
Noise Robust Hyperspectral Image Classification With MNF-Based Edge Preserving Features
Image Analysis and Stereology, 2023 Hyperspectral image (HSI) classification is an important topic in remote sensing. In this paper, we improve the principal component analysis (PCA)-based edge preserving features (EPFs) for HSI classification. We select to use minimum noise fraction (MNF)
Guangyi Chen, Adam Krzyzak, Shen-en Qian
doaj +1 more source
Remote Sensing, 2019 Hyperspectral image (HSI) classification is one of the most active topics in remote sensing. However, it is still a nontrivial task to classify the hyperspectral data accurately, since HSI always suffers from a large number of noise pixels, the ...
Fuding Xie +3 more
doaj +1 more source
IEEE Access, 2022 Recently, broad learning system (BLS) have demonstrated excellent performance in hyperspectral images (HSI) classification. However, due to the complex geometric structure and spatial layout of HSI, the linear sparse features in broad learning system are
Tuya
doaj +1 more source
Unsupervised spectral sub-feature learning for hyperspectral image classification [PDF]
, 2016 Spectral pixel classification is one of the principal techniques used in hyperspectral image (HSI) analysis. In this article, we propose an unsupervised feature learning method for classification of hyperspectral images.
De Neve, Wesley +4 more
core +1 more source
Linear vs Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Image [PDF]
, 2017 As a new machine learning approach, extreme learning machine (ELM) has received wide attentions due to its good performances. However, when directly applied to the hyperspectral image (HSI) classification, the recognition rate is too low. This is because
Cao, Faxian +4 more
core +3 more sources
O129 CLASSIFICATION OF BARRETT’S CARCINOMA SPECIMENS BY HYPERSPECTRAL IMAGING (HSI)
Diseases of the Esophagus, 2019 Abstract Aim Hyperspectral imaging (HSI) technology combines imaging with spectroscopy and can be used for the classification of malignant and non-malignant cells. Thereby HSI combined with artificial intelligent algorithms can be used to predict tumor cells in in Barrett’s carcinoma specimens.
Thieme René +5 more
openaire +1 more source