Results 21 to 30 of about 9,721 (245)
A Local Potential-Based Clustering Algorithm for Unsupervised Hyperspectral Band Selection
IEEE Access, 2019 Unsupervised band selection plays an increasingly important role in a hyperspectral image (HSI) classification because of inadequate labeling samples.
Zhaokui Li +5 more
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Methodology for determining deforestation areas in Lviv region using remote sensing data [PDF]
Advances in Geodesy and Geoinformation, 2022 The object of the study is the processing of space images on the territory of the Carpathian territory in the Lviv region, obtained from the Landsat-8 satellite. The work aims to determine the area of deforestation in the Carpathian territory of the Lviv
Borys Chetverikov +3 more
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Unsupervised Band Selection of Hyperspectral Images via Multi-Dictionary Sparse Representation
IEEE Access, 2018 Band selection is a direct and effective method to reduce the spectral dimension, which is one of popular topics in hyperspectral remote sensing. Recently, a number of methods were proposed to deal with the band selection problem.
Fei Li, Pingping Zhang, Lu Huchuan
doaj +1 more source
Unsupervised Band Selection for Hyperspectral Imagery Classification Without Manual Band Removal
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012 The rich information available in hyperspectral imagery has provided significant opportunities for material classification and identification. Due to the problem of the “curse of dimensionality” (called Hughes phenomenon) posed by the high number of spectral channels along with small amounts of labeled training samples, dimensionality reduction is a ...
Sen Jia 0001 +3 more
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High Throughput Multispectral Image Processing with Applications in Food Science. [PDF]
PLoS ONE, 2015 Recently, machine vision is gaining attention in food science as well as in food industry concerning food quality assessment and monitoring. Into the framework of implementation of Process Analytical Technology (PAT) in the food industry, image ...
Panagiotis Tsakanikas +2 more
doaj +1 more source
Unsupervised Band Selection in Hyperspectral Images using Autoencoder [PDF]
9th International Conference on Pattern Recognition Systems (ICPRS 2018), 2018 Hyperspectral images provide fine details of the observed scene from the exploitation of contiguous spectral bands. However, the high dimensionality of hyperspectral images causes a heavy burden on processing. Therefore, a common practice that has been largely adopted is the selection of bands before processing.
Habermann, Mateus +2 more
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Remote Sensing, 2022 The high spectral resolution of hyperspectral images (HSIs) provides rich information but causes data redundancy, which imposes a computational burden on practical applications.
Zhou Zhang +5 more
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Spatial residual clustering and entropy based ranking for hyperspectral band selection
European Journal of Remote Sensing, 2020 Though the Hyper-spectral images (HSI) are associated with rich spectral information for discriminating the class-specific objects, the high dimensional data generates Hughes effect for additional processing. So, during pre-processing, band Selection (BS)
Kishore Raju K. +2 more
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Remote Sensing, 2020 A comparative study of water indices and image classification algorithms for mapping inland water bodies using Landsat imagery was carried out through obtaining 24 high-resolution (≤5 m) and cloud-free images archived in Google Earth with the same (or ±1
Feifei Pan, Xiaohuan Xi, Cheng Wang
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Molecular Oncology, EarlyView.Pancreatic sensory neurons innervating healthy and PDAC tissue were retrogradely labeled and profiled by single‐cell RNA sequencing. Tumor‐associated innervation showed a dominant neurofilament‐positive subtype, altered mitochondrial gene signatures, and reduced non‐peptidergic neurons.
Elena Genova +14 more
wiley +1 more source