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 +5 more
doaj +1 more source
Optimized kernel minimum noise fraction transformation for hyperspectral image classification [PDF]
, 2017 This paper presents an optimized kernel minimum noise fraction transformation (OKMNF) for feature extraction of hyperspectral imagery. The proposed approach is based on the kernel minimum noise fraction (KMNF) transformation, which is a nonlinear ...
Gao, Lianru +4 more
core +2 more sources
Hyperspectral Band Selection via Optimal Combination Strategy
Remote Sensing, 2022 Band selection is one of the main methods of reducing the number of dimensions in a hyperspectral image. Recently, various methods have been proposed to address this issue.
Shuying Li +3 more
doaj +1 more source
Selection of the key earth observation sensors and platforms focusing on applications for Polar Regions in the scope of Copernicus system 2020-2030 [PDF]
, 2019 An optimal payload selection conducted in the frame of the H2020 ONION project (id 687490) is presented based on the ability to cover the observation needs of the Copernicus system in the time period 2020–2030.
Camps Carmona, Adriano José +6 more
core +2 more sources
CMOS compatible metamaterial absorbers for hyperspectral medium wave infrared imaging and sensing applications [PDF]
, 2018 We experimentally demonstrate a CMOS compatible medium wave infrared metal-insulator-metal (MIM) metamaterial absorber structure where for a single dielectric spacer thickness at least 93% absorption is attained for 10 separate bands centred at 3.08, 3 ...
Cumming, David S. +4 more
core +1 more source
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
openaire +1 more source
Unsupervised Band Selection Method Based on Importance-Assisted Column Subset Selection
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 +1 more source
A survey of band selection techniques for hyperspectral image classification
Journal of Spectral Imaging, 2020 Hyperspectral images usually contain hundreds of contiguous spectral bands, which can precisely discriminate the various spectrally similar classes. However, such high-dimensional data also contain highly correlated and irrelevant information, leading to
Shrutika S. Sawant, Manoharan Prabukumar
doaj +1 more source
Unsupervised Cluster-Wise Hyperspectral Band Selection for Classification
Remote Sensing, 2022 A hyperspectral image provides fine details about the scene under analysis, due to its multiple bands. However, the resulting high dimensionality in the feature space may render a classification task unreliable, mainly due to overfitting and the Hughes ...
Mateus Habermann +2 more
doaj +1 more source
Adaptive band selection snapshot multispectral imaging in the VIS/NIR domain
, 2010 Hyperspectral imaging has proven its efficiency for target detection applications but the acquisition mode and the data rate are major issues when dealing with real-time detection applications.
Ferrec, Yann +5 more
core +1 more source