Robust support vector method for hyperspectral data classification and knowledge discovery

By Gustau Camps-Valls, Luis Gómez-Chova, Javier Calpe-Maravilla, José David Martín-Guerrero, Emilio Soria-Olivas, Luis Alonso, José F. Moreno. IEEE Transactions on Geoscience and Remote Sensing, 2004

305 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/c57a50f79bf2e8174b76aed1ccabc53e9e966256

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Gustau Camps-Valls
2. Jon Atli Benediktsson
3. Luis Gómez-Chova
4. Lorenzo Bruzzone
5. Tatyana V. Bandos Marsheva
6. Frédéric Ratle
7. Jason Aaron Edward Weston
8. Farid Melgani

In Atmospheric Science

1. Jon Atli Benediktsson
2. Gustau Camps-Valls
3. Lorenzo Bruzzone
4. Yakoub Bazi
5. Farid Melgani
6. Luis Gómez-Chova
7. Frédéric Ratle
8. Jason Aaron Edward Weston

In Media Technology

1. Jon Atli Benediktsson
2. Gustau Camps-Valls
3. Lorenzo Bruzzone
4. Luis Gómez-Chova
5. Yakoub Bazi
6. Jocelyn Chanussot
7. Farid Melgani
8. Tatyana V. Bandos Marsheva

Citations in the corpus

• Composite kernels for hyperspectral image classification (2006, methodology): “However, to the authors’ knowledge, kernel methods have so far taken into account the spectral information to develop the classifier [7], [9]–[12], and thus, the spatial variability of the spectral signature has not been considered.”
• Support vector machines in remote sensing: A review (2011, unknown): “SVMs were also implemented for crop classification using HyMap hyperspectral imagery in Camps-Valls et al. (2004).”
• Semisupervised Classification of Remote Sensing Images With Active Queries (2012, unknown): “The six classes of interest are the following: “corn,” “sugar beets,” “barley,” “wheat,” “alfalfa,” and “soil” [34].”
• Classification of Hyperspectral Images With Regularized Linear Discriminant Analysis (2009, methodology): “b) DAISEX-1999 data set: This data set consists of labeled pixels of six different hyperspectral images (700 × 670 pixels) acquired with the 128-band HyMap airborne spectrometer during the DAISEX-99 campaign [5].”
• Kernel-based methods for hyperspectral image classification (2005, background): “SVMs to hyperspectral image classification see [13], [14], [16], and [17].”
• Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images (2006, background): “In [14], the authors propose to explore SVMs for crop cover classification compared with other well-known neural approaches (i.e., multilayer perceptron neural network, radial basis function neural network, and coactive neurofuzzy inference classifiers).”
• Semisupervised Neural Networks for Efficient Hyperspectral Image Classification (2010, methodology): “This technique has been applied to the field of remote sensing in [3], [25], and [41]–[45].”