Recent advances on spectral–spatial hyperspectral image classification: An overview and new guidelines
Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in the
last four decades from being a sparse research tool into a commodity product available to a …
last four decades from being a sparse research tool into a commodity product available to a …
Hyperspectral Image Classification Based on Fusing S3-PCA, 2D-SSA and Random Patch Network
Recently, the rapid development of deep learning has greatly improved the performance of
image classification. However, a central problem in hyperspectral image (HSI) classification …
image classification. However, a central problem in hyperspectral image (HSI) classification …
Breast cancer detection using deep convolutional neural networks and support vector machines
It is important to detect breast cancer as early as possible. In this manuscript, a new
methodology for classifying breast cancer using deep learning and some segmentation …
methodology for classifying breast cancer using deep learning and some segmentation …
PCA-based edge-preserving features for hyperspectral image classification
Edge-preserving features (EPFs) obtained by the application of edge-preserving filters to
hyperspectral images (HSIs) have been found very effective in characterizing significant …
hyperspectral images (HSIs) have been found very effective in characterizing significant …
Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging
Stacked autoencoders (SAEs), as part of the deep learning (DL) framework, have been
recently proposed for feature extraction in hyperspectral remote sensing. With the help of …
recently proposed for feature extraction in hyperspectral remote sensing. With the help of …
Multiscale superpixelwise prophet model for noise-robust feature extraction in hyperspectral images
Despite various approaches proposed to smooth the hyperspectral images (HSIs) before
feature extraction, the efficacy is still affected by the noise, even using the corrected dataset …
feature extraction, the efficacy is still affected by the noise, even using the corrected dataset …
Hyperspectral image classification with context-aware dynamic graph convolutional network
In hyperspectral image (HSI) classification, spatial context has demonstrated its significance
in achieving promising performance. However, conventional spatial context-based methods …
in achieving promising performance. However, conventional spatial context-based methods …
Three-dimensional singular spectrum analysis for precise land cover classification from UAV-borne hyperspectral benchmark datasets
The precise classification of land covers with hyperspectral imagery (HSI) is a major
research-focused topic in remote sensing, especially using unmanned aerial vehicle (UAV) …
research-focused topic in remote sensing, especially using unmanned aerial vehicle (UAV) …
Spatio-temporal fusion methods for spectral remote sensing: A comprehensive technical review and comparative analysis
For many years, spectral remote sensing has been essential for research on the Earth's
surface. The data from a single satellite sensor is sometimes insufficient to fulfil the …
surface. The data from a single satellite sensor is sometimes insufficient to fulfil the …
A novel band selection and spatial noise reduction method for hyperspectral image classification
As an essential reprocessing method, dimensionality reduction (DR) can reduce the data
redundancy and improve the performance of hyperspectral image (HSI) classification. A …
redundancy and improve the performance of hyperspectral image (HSI) classification. A …