Low-rank and sparse representation for hyperspectral image processing: A review
Combining rich spectral and spatial information, a hyperspectral image (HSI) can provide a
more comprehensive characterization of the Earth's surface. To better exploit HSIs, a large …
more comprehensive characterization of the Earth's surface. To better exploit HSIs, a large …
A review of unsupervised band selection techniques: Land cover classification for hyperspectral earth observation data
A hyperspectral image (HSI) is a collection of several narrow-band images that span a wide
spectral range. Each band reflects the same scene, composed of various objects imaged at …
spectral range. Each band reflects the same scene, composed of various objects imaged at …
BS-Nets: An end-to-end framework for band selection of hyperspectral image
Hyperspectral image (HSI) consists of hundreds of continuous narrowbands with high
spectral correlation, which would lead to the so-called Hughes phenomenon and the high …
spectral correlation, which would lead to the so-called Hughes phenomenon and the high …
Remote sensing image spatiotemporal fusion using a generative adversarial network
H Zhang, Y Song, C Han… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Due to technological limitations and budget constraints, spatiotemporal fusion is considered
a promising way to deal with the tradeoff between the temporal and spatial resolutions of …
a promising way to deal with the tradeoff between the temporal and spatial resolutions of …
Hyperspectral image denoising with total variation regularization and nonlocal low-rank tensor decomposition
Hyperspectral images (HSIs) are normally corrupted by a mixture of various noise types,
which degrades the quality of the acquired image and limits the subsequent application. In …
which degrades the quality of the acquired image and limits the subsequent application. In …
Accessing the temporal and spectral features in crop type mapping using multi-temporal Sentinel-2 imagery: A case study of Yi'an County, Heilongjiang province …
Crop type mapping visualizes the spatial distribution patterns and proportions of the
cultivated areas with different crop types, and is the basis for subsequent agricultural …
cultivated areas with different crop types, and is the basis for subsequent agricultural …
Dimensionality reduction of hyperspectral imagery based on spatial–spectral manifold learning
The graph embedding (GE) methods have been widely applied for dimensionality reduction
of hyperspectral imagery (HSI). However, a major challenge of GE is how to choose the …
of hyperspectral imagery (HSI). However, a major challenge of GE is how to choose the …
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 …
Hyperspectral band selection via adaptive subspace partition strategy
Band selection is considered as a direct and effective method to reduce redundancy, which
is to select some informative and distinctive bands from the original hyperspectral image …
is to select some informative and distinctive bands from the original hyperspectral image …
Graph convolutional subspace clustering: A robust subspace clustering framework for hyperspectral image
Hyperspectral image (HSI) clustering is a challenging task due to the high complexity of HSI
data. Subspace clustering has been proven to be powerful for exploiting the intrinsic …
data. Subspace clustering has been proven to be powerful for exploiting the intrinsic …