Results 21 to 30 of about 3,440,683 (277)
Bathymetric-Based Band Selection Method for Hyperspectral Underwater Target Detection
Remote Sensing, 2021 Band selection has imposed great impacts on hyperspectral image processing in recent years. Unfortunately, few existing methods are proposed for hyperspectral underwater target detection (HUTD).
Jiahao Qi +6 more
doaj +1 more source
vfonsecad/unsupervised-sample-selection: publication
, 2021 This repository is maintained at https://github.com/vfonsecad/unsupervised-sample ...
Bart De Ketelaere +3 more
core +1 more source
Band Ranking via Extended Coefficient of Variation for Hyperspectral Band Selection
Remote Sensing, 2020 Hundreds of narrow bands over a continuous spectral range make hyperspectral imagery rich in information about objects, while at the same time causing the neighboring bands to be highly correlated.
Peifeng Su +2 more
doaj +1 more source
, 2021 Band selection is an effective means to alleviate the curse of dimensionality in hyperspectral data. Many methods select a compact and low redundant band subset, which is inadequate as it may degrade the classification performance. Instead, more emphasis
Mei, Xiaoguang +6 more
core +1 more source
EBARec-BS: Effective Band Attention Reconstruction Network for Hyperspectral Imagery Band Selection
Remote Sensing, 2021 Hyperspectral band selection (BS) is an effective means to avoid the Hughes phenomenon and heavy computational burden in hyperspectral image processing.
Yufei Liu +3 more
doaj +1 more source
Unsupervised Texture Segmentation using Active Contours and Local Distributions of Gaussian Markov Random Field Parameters [PDF]
, 2012 In this paper, local distributions of low order Gaussian Markov Random Field (GMRF) model parameters are proposed as texture features for unsupervised texture segmentation.Instead of using model parameters as texture features, we exploit the variations ...
Michael Bennet +7 more
core +2 more sources
Unsupervised Hyperspectral Band Selection using Clustering and Single-Layer Neural Network
Revue Française de Photogrammétrie et de Télédétection, 2018 Hyperspectral images provide rich spectral details of the observed scene by exploiting contiguous bands. But, the processing of such images becomes heavy, due to the high dimensionality.
Mateus Habermann +2 more
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
openaire +2 more sources
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
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingBand selection is a critical step in processing hyperspectral imagery (HSI); reducing input dimensionality allows models to mitigate redundancy, enhance computational efficiency, and improve learning accuracy.
Jacqueline Liu +2 more
doaj +1 more source