Deep learning for classification of hyperspectral data: A comparative review
In recent years, deep-learning techniques revolutionized the way remote sensing data are
processed. The classification of hyperspectral data is no exception to the rule, but it has …
processed. The classification of hyperspectral data is no exception to the rule, but it has …
Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the art
Recent advances in airborne and spaceborne hyperspectral imaging technology have
provided end users with rich spectral, spatial, and temporal information. They have made a …
provided end users with rich spectral, spatial, and temporal information. They have made a …
Spectral–spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach
W Zhao, S Du - IEEE Transactions on Geoscience and Remote …, 2016 - ieeexplore.ieee.org
In this paper, we propose a spectral–spatial feature based classification (SSFC) framework
that jointly uses dimension reduction and deep learning techniques for spectral and spatial …
that jointly uses dimension reduction and deep learning techniques for spectral and spatial …
Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches
Imaging spectrometers measure electromagnetic energy scattered in their instantaneous
field view in hundreds or thousands of spectral channels with higher spectral resolution than …
field view in hundreds or thousands of spectral channels with higher spectral resolution than …
Hyperspectral image spatial super-resolution via 3D full convolutional neural network
Hyperspectral images are well-known for their fine spectral resolution to discriminate
different materials. However, their spatial resolution is relatively low due to the trade-off in …
different materials. However, their spatial resolution is relatively low due to the trade-off in …
A review of nonlinear hyperspectral unmixing methods
In hyperspectral unmixing, the prevalent model used is the linear mixing model, and a large
variety of techniques based on this model has been proposed to obtain endmembers and …
variety of techniques based on this model has been proposed to obtain endmembers and …
Total variation spatial regularization for sparse hyperspectral unmixing
Spectral unmixing aims at estimating the fractional abundances of pure spectral signatures
(also called endmembers) in each mixed pixel collected by a remote sensing hyperspectral …
(also called endmembers) in each mixed pixel collected by a remote sensing hyperspectral …
Sparse unmixing of hyperspectral data
Linear spectral unmixing is a popular tool in remotely sensed hyperspectral data
interpretation. It aims at estimating the fractional abundances of pure spectral signatures …
interpretation. It aims at estimating the fractional abundances of pure spectral signatures …
Advances in hyperspectral remote sensing of vegetation and agricultural crops
Hyperspectral data (Table 1) is acquired as continuous narrowbands (eg, each band with 1
to 10 nanometer or nm bandwidths) over a range of electromagnetic spectrum (eg, 400 …
to 10 nanometer or nm bandwidths) over a range of electromagnetic spectrum (eg, 400 …
Vertex component analysis: A fast algorithm to unmix hyperspectral data
Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture
analysis, or linear unmixing, aims at estimating the number of reference substances, also …
analysis, or linear unmixing, aims at estimating the number of reference substances, also …