Band selection strategies for hyperspectral image classification based on machine learning and artificial intelligent techniques–Survey
SS Sawant, P Manoharan, A Loganathan - Arabian Journal of …, 2021 - Springer
As the hyperspectral image consists of hundreds of highly correlated spectral bands, the
selection of informative and highly discriminative bands is necessary for hyperspectral …
selection of informative and highly discriminative bands is necessary for hyperspectral …
SemiFREE: semisupervised feature selection with fuzzy relevance and redundancy
K Liu, T Li, X Yang, H Chen, J Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Feature selection, as an effective dimensionality reduction technique, is favored in
preprocessing data. However, most existing algorithms are solely liable for labeled or …
preprocessing data. However, most existing algorithms are solely liable for labeled or …
Deep reinforcement learning for semisupervised hyperspectral band selection
Band selection is an important step in efficient processing of hyperspectral images (HSIs),
which can be seen as the combination of powerful band search technique and effective …
which can be seen as the combination of powerful band search technique and effective …
Hyperspectral band selection via optimal neighborhood reconstruction
Band selection is one of the most important technique in the reduction of hyperspectral
image (HSI). Different from traditional feature selection problem, an important characteristic …
image (HSI). Different from traditional feature selection problem, an important characteristic …
Spatial and spectral structure preserved self-representation for unsupervised hyperspectral band selection
As an effective manner to reduce data redundancy and processing inconvenience,
hyperspectral band selection aims to select a subset of informative and discriminative bands …
hyperspectral band selection aims to select a subset of informative and discriminative bands …
A hybrid gray wolf optimizer for hyperspectral image band selection
Y Wang, Q Zhu, H Ma, H Yu - IEEE Transactions on Geoscience …, 2022 - ieeexplore.ieee.org
High spectral dimensionality of hyperspectral image (HSI) has brought great redundancy for
data processing. Band selection (BS), as one of the most commonly used dimension …
data processing. Band selection (BS), as one of the most commonly used dimension …
MR-selection: A meta-reinforcement learning approach for zero-shot hyperspectral band selection
Band selection is an effective method to deal with the difficulties in image transmission,
storage, and processing caused by redundant and noisy bands in hyperspectral images …
storage, and processing caused by redundant and noisy bands in hyperspectral images …
Diagnosis and classification of Parkinson's disease using ensemble learning and 1D-PDCovNN
In this paper, we proposed a novel approach to diagnose and classify Parkinson's Disease
(PD) using ensemble learning and 1D-PDCovNN, a novel deep learning technique. PD is a …
(PD) using ensemble learning and 1D-PDCovNN, a novel deep learning technique. PD is a …
Unsupervised hyperspectral band selection via hybrid graph convolutional network
Hyperspectral image (HSI) provided with a substantial number of correlated bands causes
calculation consumption and an undesirable “dimension disaster” problem for the …
calculation consumption and an undesirable “dimension disaster” problem for the …
Dual-graph convolutional network based on band attention and sparse constraint for hyperspectral band selection
Band selection is a research hotspot in hyperspectral image processing. The continuity of
the spectral bands causes the adjacent bands to be highly correlated, and correlation …
the spectral bands causes the adjacent bands to be highly correlated, and correlation …