Results 11 to 20 of about 3,440,683 (277)

Segmented Autoencoders for Unsupervised Embedded Hyperspectral Band Selection [PDF]

2018 7th European Workshop on Visual Information Processing (EUVIP), 2018
One of the major challenges in hyperspectral imaging (HSI) is the selection of the most informative wavelengths within the vast amount of data in a hypercube. Band selection can reduce the amount of data and computational cost as well as counteracting the negative effects of redundant and erroneous information. In this paper, we propose an unsupervised,
Tschannerl, Julius, Ren, Jinchang, Zabalza, Jaime, Marshall, Stephen   +3 more
core   +8 more sources

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, Frémont, Vincent, Shiguemori, Elcio H.   +2 more
openaire   +4 more sources

Two-Stage Unsupervised Hyperspectral Band Selection Based on Deep Reinforcement Learning

Remote Sensing
Hyperspectral images are high-dimensional data that capture detailed spectral information across a wide range of wavelengths, enabling the precise identification and analysis of different materials or objects. However, the high dimensionality of the data
Yi Guo, Qianqian Wang, Bingliang Hu, Xueming Qian, Haibo Ye   +4 more
doaj   +3 more sources

Rapid FTIR Spectral Fingerprinting of Kidney Allograft Perfusion Fluids Distinguishes DCD from DBD Donors: A Pilot Machine Learning Study [PDF]

Metabolites
Background/Objectives: Rapid, objective phenotyping of donor kidneys is needed to support peri-implant decisions. Label-free Fourier-transform infrared (FTIR) spectroscopy of static cold-storage Celsior® perfusion fluid can discriminate kidneys recovered
Luis Ramalhete, Rúben Araújo, Miguel Bigotte Vieira, Emanuel Vigia, Ana Pena, Sofia Carrelha, Anibal Ferreira, Cecília R. C. Calado   +7 more
doaj   +2 more sources

Combination of Clustering and Ranking Techniques for Unsupervised Band Selection of Hyperspectral Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015
Curse of dimensionality is a major disadvantage for classification of hyperspectral imagery since a large number of bands need to be dealt with. Band selection is a task to reduce the number of bands. An unsupervised band selection method is proposed in this article. It is a three-step procedure.
Susmita Ghosh, Ashish Ghosh, Aloke Datta   +2 more
exaly   +3 more sources

Unsupervised hyperspectral band selection in the compressive sensing domain [PDF]

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 2019
Band selection (BS) algorithms are an effective means of reducing the high volume of redundant data produced by the hundreds of contiguous spectral bands of Hyperspectral images (HSI).
Bernard Lampe, Adam Bekit, Charles Della Porta, Bai Xue, Chein-I Chang   +4 more
openaire   +2 more sources

Hyperspectral Band Selection via Band Grouping and Adaptive Multi-Graph Constraint

Remote Sensing, 2022
Unsupervised band selection has gained increasing attention recently since massive unlabeled high-dimensional data often need to be processed in the domains of machine learning and data mining.
Mengbo You, Xiancheng Meng, Yishu Wang, Hongyuan Jin, Chunting Zhai, Aihong Yuan   +5 more
doaj   +1 more source

Fractal Autoencoder-Based Unsupervised Hyperspectral Bands Selection for Remote Sensing Land-Cover Classification

Engineering Proceedings, 2023
Band selection is a frequently used dimension reduction technique for hyperspectral images (HSI) to address the “curse of dimensionality” phenomenon in machine learning (ML). This technique identifies and selects a subset of the most important bands from
Sara Benali, Mohammed El Amin Larabi, Dalila Attaf   +2 more
doaj   +1 more source

Robust Unsupervised Hyperspectral Band Selection via Global Affinity Matrix Reconstruction

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
Unsupervised band selection is fundamental to alleviate the curse of dimensionality for hyperspectral imagery. Although many research works have been developed, it is still a challenging problem to improve the poor classification performance with a small
Mengbo You, Aihong Yuan, Min Zou, Kouichi Konno   +3 more
doaj   +1 more source

Unsupervised hyperspectral band selection using parallel processing [PDF]

2009 IEEE International Geoscience and Remote Sensing Symposium, 2009
Band selection is a common technique to reducing the data dimensionality of hyperspectral imagery. When the desired object information is unknown, the objective of an unsupervised band selection approach is to select the most distinctive and informative bands. Although band selection can significantly alleviate the computational burden in the following
He Yang, Qian Du 0001
openaire   +2 more sources