Results 11 to 20 of about 3,935,477 (223)
Multiobjective Optimization-Based Hyperspectral Unsupervised Band Selection for Anomaly Detection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingBand selection (BS) is a critical topic in hyperspectral image dimensionality reduction, focusing on identifying representative bands that can convey the essential information of the full bands without significant loss.
Shihui Liu +4 more
doaj +4 more sources
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 +3 more
core +8 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 +4 more
openaire +2 more sources
Unsupervised Band Selection Method Based on Importance-Assisted Column Subset Selection [PDF]
IEEE Access, 2019 Band selection is an important preprocessing technique for hyperspectral images to select a band subset with representative information and low correlation. However, most methods focus on removing redundant components without loss of original information,
Xiaoyan Luo +3 more
doaj +3 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.
M. Habermann +2 more
openaire +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 +2 more sources
Maximum simplex volume: an efficient unsupervised band selection method for hyperspectral image
IET Computer Vision, 2019 Hyperspectral imaging makes it possible to obtain object information with fine spectral resolution as well as spatial resolution, which is beneficial to a wide array of applications. However, there is a high correlation among the bands in a hyperspectral
Xuefeng Jiang +3 more
doaj +2 more sources
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
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
, 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 +13 more
core +1 more source