Multimodal hyperspectral remote sensing: An overview and perspective
Y Gu, T Liu, G Gao, G Ren, Y Ma, J Chanussot… - Science China …, 2021 - Springer
Since the advent of hyperspectral remote sensing in the 1980s, it has made important
achievements in aerospace and aviation field and been applied in many fields …
achievements in aerospace and aviation field and been applied in many fields …
An augmented linear mixing model to address spectral variability for hyperspectral unmixing
Hyperspectral imagery collected from airborne or satellite sources inevitably suffers from
spectral variability, making it difficult for spectral unmixing to accurately estimate abundance …
spectral variability, making it difficult for spectral unmixing to accurately estimate abundance …
SuperPCA: A superpixelwise PCA approach for unsupervised feature extraction of hyperspectral imagery
As an unsupervised dimensionality reduction method, the principal component analysis
(PCA) has been widely considered as an efficient and effective preprocessing step for …
(PCA) has been widely considered as an efficient and effective preprocessing step for …
Information-theoretic feature selection with segmentation-based folded principal component analysis (PCA) for hyperspectral image classification
Hyperspectral image (HSI) usually holds information of land cover classes as a set of many
contiguous narrow spectral wavelength bands. For its efficient thematic mapping or …
contiguous narrow spectral wavelength bands. For its efficient thematic mapping or …
Local geometric structure feature for dimensionality reduction of hyperspectral imagery
Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data
and separate the interclass data, and it is very useful to analyze the high-dimensional data …
and separate the interclass data, and it is very useful to analyze the high-dimensional data …
A sparse and low-rank near-isometric linear embedding method for feature extraction in hyperspectral imagery classification
A sparse and low-rank near-isometric linear embedding (SLRNILE) method has been
proposed to make dimensionality reduction and extract proper features for hyperspectral …
proposed to make dimensionality reduction and extract proper features for hyperspectral …
Hyperspectral image classification with robust sparse representation
Recently, the sparse representation-based classification (SRC) methods have been
successfully used for the classification of hyperspectral imagery, which relies on the …
successfully used for the classification of hyperspectral imagery, which relies on the …
Centroid and covariance alignment-based domain adaptation for unsupervised classification of remote sensing images
L Ma, MM Crawford, L Zhu, Y Liu - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
A new domain adaptation algorithm based on the class centroid and covariance alignment
(CCCA) is proposed for classification of remote sensing images. This approach exploits both …
(CCCA) is proposed for classification of remote sensing images. This approach exploits both …
Effective feature extraction through segmentation-based folded-PCA for hyperspectral image classification
The remote sensing hyperspectral images (HSIs) usually comprise many important
information of the land covers capturing through a set of hundreds of narrow and contiguous …
information of the land covers capturing through a set of hundreds of narrow and contiguous …
ES2FL: Ensemble Self-Supervised Feature Learning for Small Sample Classification of Hyperspectral Images
B Liu, K Gao, A Yu, L Ding, C Qiu, J Li - Remote Sensing, 2022 - mdpi.com
Classification with a few labeled samples has always been a longstanding problem in the
field of hyperspectral image (HSI) processing and analysis. Aiming at the small sample …
field of hyperspectral image (HSI) processing and analysis. Aiming at the small sample …