Optimal Clustering Framework for Hyperspectral Band Selection [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2018 Band selection, by choosing a set of representative bands in hyperspectral image (HSI), is an effective method to reduce the redundant information without compromising the original contents. Recently, various unsupervised band selection methods have been proposed, but most of them are based on approximation algorithms which can only obtain suboptimal ...
Qi Wang 0009 +2 more
openaire +2 more sources
Fuzzy spectral and spatial feature integration for classification of nonferrous materials in hyperspectral data [PDF]
, 2009 Hyperspectral data allows the construction of more elaborate models to sample the properties of the nonferrous materials than the standard RGB color representation.
Iriondo, Pedro M. +4 more
core +2 more sources
A Structural Subspace Clustering Approach for Hyperspectral Band Selection [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2022 Band selection, which removes irrelevant bands from hyperspectral images (HSIs) and keeps essential spectral information contained in a relatively few bands, allows huge savings in data storage, computation time, and imaging hardware. In this article, we propose a novel structural subspace clustering (STSC) method for hyperspectral band selection ...
Shaoguang Huang +2 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 +5 more
doaj +1 more source
Background-Aware Band Selection for Object Tracking in Hyperspectral Videos
, 2023 Hyperspectral images contain many bands that can be used to obtain object material information for object tracking and remote sensing. Nevertheless, neighboring bands of hyperspectral images are often highly correlated, and a large number of bands ...
Islam, MA, Zhang, W, Gao, Y, Zhou, J
core +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
Customizing kernel functions for SVM-based hyperspectral image classification [PDF]
, 2008 Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available algorithms.
Damper, R. I. +7 more
core +2 more sources
Hyperspectral Band Selection for Crop Identification and Mapping of Agriculture
Remote SensingDifferent crops, as well as the same crop at different growth stages, display distinct spectral and spatial characteristics in hyperspectral images (HSIs) due to variations in their chemical composition and structural features.
Yulei Tan +8 more
semanticscholar +1 more source