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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 …
Hyperspectral image classification: Potentials, challenges, and future directions
Recent imaging science and technology discoveries have considered hyperspectral
imagery and remote sensing. The current intelligent technologies, such as support vector …
imagery and remote sensing. The current intelligent technologies, such as support vector …
Hyperspectral image classification with deep feature fusion network
Recently, deep learning has been introduced to classify hyperspectral images (HSIs) and
achieved good performance. In general, deep models adopt a large number of hierarchical …
achieved good performance. In general, deep models adopt a large number of hierarchical …
New frontiers in spectral-spatial hyperspectral image classification: The latest advances based on mathematical morphology, Markov random fields, segmentation …
In recent years, airborne and spaceborne hyperspectral imaging systems have advanced in
terms of spectral and spatial resolution, which makes the data sets they produce a valuable …
terms of spectral and spatial resolution, which makes the data sets they produce a valuable …
Learning compact and discriminative stacked autoencoder for hyperspectral image classification
As one of the fundamental research topics in remote sensing image analysis, hyperspectral
image (HSI) classification has been extensively studied so far. However, how to …
image (HSI) classification has been extensively studied so far. However, how to …
A new spatial–spectral feature extraction method for hyperspectral images using local covariance matrix representation
In this paper, a novel local covariance matrix (CM) representation method is proposed to
fully characterize the correlation among different spectral bands and the spatial-contextual …
fully characterize the correlation among different spectral bands and the spatial-contextual …
Maximum likelihood estimation-based joint sparse representation for the classification of hyperspectral remote sensing images
A joint sparse representation (JSR) method has shown superior performance for the
classification of hyperspectral images (HSIs). However, it is prone to be affected by outliers …
classification of hyperspectral images (HSIs). However, it is prone to be affected by outliers …
Hyperspectral image classification via multiple-feature-based adaptive sparse representation
A multiple-feature-based adaptive sparse representation (MFASR) method is proposed for
the classification of hyperspectral images (HSIs). The proposed method mainly includes the …
the classification of hyperspectral images (HSIs). The proposed method mainly includes the …
Self-paced joint sparse representation for the classification of hyperspectral images
In this paper, a self-paced joint sparse representation (SPJSR) model is proposed for the
classification of hyperspectral images (HSIs). It replaces the least-squares (LS) loss in the …
classification of hyperspectral images (HSIs). It replaces the least-squares (LS) loss in the …
Classification of hyperspectral images by Gabor filtering based deep network
In this paper, a novel spectral-spatial classification method based on Gabor filtering and
deep network (GFDN) is proposed. First, Gabor features are extracted by performing Gabor …
deep network (GFDN) is proposed. First, Gabor features are extracted by performing Gabor …