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 …
Meta-hashing for remote sensing image retrieval
With the explosive growth of the volume and resolution of high-resolution remote-sensing
(HRRS) images, the management of them becomes a challenging task. The traditional …
(HRRS) images, the management of them becomes a challenging task. The traditional …
Robust dual graph self-representation for unsupervised hyperspectral band selection
Unsupervised band selection aims to select informative spectral bands to preprocess
hyperspectral images (HSIs) without using labels. Traditional band selection methods only …
hyperspectral images (HSIs) without using labels. Traditional band selection methods only …
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 …
Hyperspectral band selection via region-aware latent features fusion based clustering
Band selection is one of the most effective methods to reduce the band redundancy of
hyperspectral images (HSIs). Most existing band selection methods tend to regard each …
hyperspectral images (HSIs). Most existing band selection methods tend to regard each …
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 …
Self-supervised divide-and-conquer generative adversarial network for classification of hyperspectral images
Generative adversarial network (GAN) has been rapidly developed because of its powerful
generating ability. However, imbalanced class distribution of hyperspectral images (HSIs) …
generating ability. However, imbalanced class distribution of hyperspectral images (HSIs) …
Dual collaborative constraints regularized low-rank and sparse representation via robust dictionaries construction for hyperspectral anomaly detection
S Lin, M Zhang, X Cheng, K Zhou… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
The low rank and sparse representation (LRSR) technique has attracted increasing attention
for hyperspectral anomaly detection (HAD). Although a large quantity of research based on …
for hyperspectral anomaly detection (HAD). Although a large quantity of research based on …
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 …
Multiscale representation learning for image classification: A survey
Feature representation has been widely used and developed recently. Multiscale features
have led to remarkable breakthroughs for representation learning process in many computer …
have led to remarkable breakthroughs for representation learning process in many computer …