Hyperspectral image classification—Traditional to deep models: A survey for future prospects
Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications
because it benefits from the detailed spectral information contained in each pixel. Notably …
because it benefits from the detailed spectral information contained in each pixel. Notably …
Conventional to deep ensemble methods for hyperspectral image classification: A comprehensive survey
Hyperspectral image classification (HSIC) has become a hot research topic. Hyperspectral
imaging (HSI) has been widely used in a wide range of real-world application areas due to …
imaging (HSI) has been widely used in a wide range of real-world application areas due to …
Self-supervised locality preserving low-pass graph convolutional embedding for large-scale hyperspectral image clustering
Y Ding, Z Zhang, X Zhao, Y Cai, S Li… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Due to prior knowledge deficiency, large spectral variability, and high dimension of
hyperspectral image (HSI), HSI clustering is extremally a fundamental but challenging task …
hyperspectral image (HSI), HSI clustering is extremally a fundamental but challenging task …
Domain adaptation in remote sensing image classification: A survey
Traditional remote sensing (RS) image classification methods heavily rely on labeled
samples for model training. When labeled samples are unavailable or labeled samples have …
samples for model training. When labeled samples are unavailable or labeled samples have …
Unsupervised self-correlated learning smoothy enhanced locality preserving graph convolution embedding clustering for hyperspectral images
Y Ding, Z Zhang, X Zhao, W Cai, N Yang… - … on Geoscience and …, 2022 - ieeexplore.ieee.org
Hyperspectral image (HSI) clustering is an extremely fundamental but challenging task with
no labeled samples. Deep clustering methods have attracted increasing attention and have …
no labeled samples. Deep clustering methods have attracted increasing attention and have …
A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers
Hyperspectral Image Classification (HSC) is a challenging task due to the high
dimensionality and complex nature of Hyperspectral (HS) data. Traditional Machine …
dimensionality and complex nature of Hyperspectral (HS) data. Traditional Machine …
Deep spatial-spectral subspace clustering for hyperspectral image
Hyperspectral image (HSI) clustering is a challenging task due to the complex
characteristics in HSI data, such as spatial-spectral structure, high-dimension, and large …
characteristics in HSI data, such as spatial-spectral structure, high-dimension, and large …
Collaborative learning of lightweight convolutional neural network and deep clustering for hyperspectral image semi-supervised classification with limited training …
Deep learning provides excellent potentials for hyperspectral images (HSIs) classification,
but it is infamous for requiring large amount of labeled samples while the collection of high …
but it is infamous for requiring large amount of labeled samples while the collection of high …
Semisupervised hyperspectral image classification using a probabilistic pseudo-label generation framework
Deep neural networks (DNNs) show impressive performance for hyperspectral image (HSI)
classification when abundant labeled samples are available. The problem is that HSI …
classification when abundant labeled samples are available. The problem is that HSI …
Spectral–spatial exploration for hyperspectral image classification via the fusion of fully convolutional networks
Due to its remarkable feature representation capability and high performance, convolutional
neural networks (CNN) have emerged as a popular choice for hyperspectral image (HSI) …
neural networks (CNN) have emerged as a popular choice for hyperspectral image (HSI) …