Composite kernels for hyperspectral image classification

By Gustau Camps-Valls, Luis Gómez-Chova, Jordi Muñoz-Marí, Joan Vila-Francés, Javier Calpe-Maravilla. IEEE Geoscience and Remote Sensing Letters, 2006

1,095 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/14b64bb5af95367883abd2b95d95979f87bcd5f0

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Citations in the corpus

Deep Learning for Classification of Hyperspectral Data: A Comparative Review (2019, neutral): “In particular, [33] introduced the possibility of designing spatial–spectral kernels for SVMs able to handle hyperspectral data.”
Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning (2014, neutral): “1 a = = / to create a unified Gram matrix that characterizes relations across different input sources [23].”
Support vector machines in remote sensing: A review (2011, neutral): “Camps-Valls et al. (2006a) investigated a RVM-based approach, a variant of SVMs, in order to lessen the uncertainty inherent in handling satellite-derived and in-situ measurements of oceanic chlorophyll concentration.”
Spectral–Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation (2014, methodology): “In [15] and [16], the spectral and spatial information of each HSI pixel is combined via a composite kernel approach.”
Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles (2008, methodology): “The proposed approach is different from approaches in [12]–[14], where the authors had extracted spatial information and used composite kernel to include both type of information.”
Mean Map Kernel Methods for Semisupervised Cloud Classification (2010, methodology): “In [54], we explicitly formulated a full family of kernel-based classifiers”
A Survey on Spectral–Spatial Classification Techniques Based on Attribute Profiles (2015, methodology): “A linearly weighted composite kernel is a weighted combination of different kernels computed using the available features [73].”
Fusion of Support Vector Machines for Classification of Multisensor Data (2007, neutral): “In [26], composite kernels were introduced for”
SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images (2010, methodology): “For instance, spectral and spatial information can be combined within a feature vector of each pixel, and then, a pixelwise classiﬁcation technique can be applied to the obtained set of vectors [6], [9].”
Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods (2013, background): “The technological evolution of optical sensors over the last few decades has provided remote sensing analysts with rich spatial, spectral, and temporal information.”
Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection (2008, methodology): “In this section, we briefly review the main properties of Mercer’s kernels and the standard formulations for the binary SVC and for the one-class SVDD used in this paper, which have demonstrated excellent characteristics in problems with a low number of high-dimensional training samples [30], [31].”
Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network (2019, methodology): “Based on SVM, the morphological kernel [16] and the composite kernel [17] methods were also proposed to exploit spectral-spatial information.”
Spectral–Spatial Graph Convolutional Networks for Semisupervised Hyperspectral Image Classification (2019, methodology): “The RBF-kernel for SVM [1] and SVMC K [20] is used, and the parameters are obtained via fivefold cross validation.”
Hyperspectral Image Classification With Deep Feature Fusion Network (2018, background): “, composite kernel [12] and morphological kernel [13], [14]) based on spectral–spatial information is designed to improve the SVM classifier.”
Generalized Composite Kernel Framework for Hyperspectral Image Classification (2013, methodology): “main future directions identified in [17].”
Multiple Feature Learning for Hyperspectral Image Classification (2015, methodology): “In our second experiment, we compare the proposed framework with CK learning [24] and GCK learning [25].”