Dimensionality reduction strategies for land use land cover classification based on airborne hyperspectral imagery: a survey
MA Moharram, DM Sundaram - Environmental Science and Pollution …, 2023 - Springer
Hyperspectral image (HSI) contains hundreds of adjacent spectral bands, which can
effectively differentiate the region of interest. Nevertheless, many irrelevant and highly …
effectively differentiate the region of interest. Nevertheless, many irrelevant and highly …
A review of unsupervised band selection techniques: Land cover classification for hyperspectral earth observation data
A hyperspectral image (HSI) is a collection of several narrow-band images that span a wide
spectral range. Each band reflects the same scene, composed of various objects imaged at …
spectral range. Each band reflects the same scene, composed of various objects imaged at …
Optimal clustering framework for hyperspectral band selection
Band selection, by choosing a set of representative bands in a hyperspectral image, is an
effective method to reduce the redundant information without compromising the original …
effective method to reduce the redundant information without compromising the original …
[HTML][HTML] Land use mapping using Sentinel-1 and Sentinel-2 time series in a heterogeneous landscape in Niger, Sahel
Land use maps describe the spatial distribution of natural resources, cultural landscapes,
and human settlements, serving as an important planning tool for decision makers. In the …
and human settlements, serving as an important planning tool for decision makers. In the …
Hyperspectral image band selection based on CNN embedded GA (CNNeGA)
M Esmaeili, D Abbasi-Moghadam… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Hyperspectral images (HSIs) are a powerful source of reliable data in various remote
sensing applications. But due to the large number of bands, HSI has information …
sensing applications. But due to the large number of bands, HSI has information …
[HTML][HTML] Cotton classification method at the county scale based on multi-features and random forest feature selection algorithm and classifier
H Fei, Z Fan, C Wang, N Zhang, T Wang, R Chen… - Remote Sensing, 2022 - mdpi.com
Accurate cotton maps are crucial for monitoring cotton growth and precision management.
The paper proposed a county-scale cotton mapping method by using random forest (RF) …
The paper proposed a county-scale cotton mapping method by using random forest (RF) …
Automatic graph learning convolutional networks for hyperspectral image classification
The excellent performance of graph convolutional networks (GCNs) on non-Euclidean data
has drawn widespread attention from the hyperspectral image classification (HSIC) …
has drawn widespread attention from the hyperspectral image classification (HSIC) …
Unsupervised feature extraction in hyperspectral images based on Wasserstein generative adversarial network
Feature extraction (FE) is a crucial research area in hyperspectral image (HSI) processing.
Recently, due to the powerful ability of deep learning (DL) to extract spatial and spectral …
Recently, due to the powerful ability of deep learning (DL) to extract spatial and spectral …
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 …
Hyperspectral band selection using attention-based convolutional neural networks
Hyperspectral imaging has become a mature technology which brings exciting possibilities
in various domains, including satellite image analysis. However, the high dimensionality and …
in various domains, including satellite image analysis. However, the high dimensionality and …