Attention-based adaptive spectral–spatial kernel ResNet for hyperspectral image classification
Hyperspectral images (HSIs) provide rich spectral–spatial information with stacked
hundreds of contiguous narrowbands. Due to the existence of noise and band correlation …
hundreds of contiguous narrowbands. Due to the existence of noise and band correlation …
Deep neural networks-based relevant latent representation learning for hyperspectral image classification
The classification of hyperspectral image is a challenging task due to the high dimensional
space, with large number of spectral bands, and low number of labeled training samples. To …
space, with large number of spectral bands, and low number of labeled training samples. To …
Machine learning and deep learning techniques for spectral spatial classification of hyperspectral images: A comprehensive survey
The growth of Hyperspectral Image (HSI) analysis is due to technology advancements that
enable cameras to collect hundreds of continuous spectral information of each pixel in an …
enable cameras to collect hundreds of continuous spectral information of each pixel in an …
SpaSSA: Superpixelwise adaptive SSA for unsupervised spatial–spectral feature extraction in hyperspectral image
Singular spectral analysis (SSA) has recently been successfully applied to feature extraction
in hyperspectral image (HSI), including conventional (1-D) SSA in spectral domain and 2-D …
in hyperspectral image (HSI), including conventional (1-D) SSA in spectral domain and 2-D …
Multimodal self-supervised learning for remote sensing data land cover classification
Z Xue, G Yang, X Yu, A Yu, Y Guo, B Liu, J Zhou - Pattern Recognition, 2025 - Elsevier
Deep learning has revolutionized the remote sensing image processing techniques over the
past few years. Nevertheless, annotating high-quality samples is difficult and time …
past few years. Nevertheless, annotating high-quality samples is difficult and time …
Unsupervised band selection based on weighted information entropy and 3D discrete cosine transform for hyperspectral image classification
SS Sawant, P Manoharan - International Journal of Remote …, 2020 - Taylor & Francis
Band selection is an effective means of reducing the dimensionality of the hyperspectral
image by selecting the most informative and distinctive bands. Bands are usually selected …
image by selecting the most informative and distinctive bands. Bands are usually selected …
Novel hyperbolic clustering-based band hierarchy (HCBH) for effective unsupervised band selection of hyperspectral images
For dimensionality reduction of HSI, many clustering-based unsupervised band selection
(UBS) methods have been proposed due to their superiority of reducing the high …
(UBS) methods have been proposed due to their superiority of reducing the high …
MIMR-DGSA: Unsupervised hyperspectral band selection based on information theory and a modified discrete gravitational search algorithm
Band selection plays an important role in hyperspectral data analysis as it can improve the
performance of data analysis without losing information about the constitution of the …
performance of data analysis without losing information about the constitution of the …
Nondestructive phenolic compounds measurement and origin discrimination of peated barley malt using near-infrared hyperspectral imagery and machine learning
Quantifying phenolic compound in peated barley malt and discriminating its origin are
essential to maintain the aroma of high-quality Scottish whisky during the manufacturing …
essential to maintain the aroma of high-quality Scottish whisky during the manufacturing …
Spatial and class structure regularized sparse representation graph for semi-supervised hyperspectral image classification
Constructing a good graph that can capture intrinsic data structures is critical for graph-
based semi-supervised learning methods, which are widely applied for hyperspectral image …
based semi-supervised learning methods, which are widely applied for hyperspectral image …