Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations

By Antonio J. Plaza, Pablo Martínez, Javier Plaza, Rosa M. Pérez. IEEE Transactions on Geoscience and Remote Sensing, 2005

423 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/1154f967a98c6f0a629a00696305e9083d86dd5a

How researchers see this paper

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

In Atmospheric Science

1. Liangpei Zhang
2. Jon Atli Benediktsson
3. Wenzhi Zhao
4. Lefei Zhang
5. Bo Du
6. Shutao Li
7. Shihong Du
8. Jocelyn Chanussot

In Ecology

1. Liangpei Zhang
2. Lefei Zhang
3. Jon Atli Benediktsson
4. Wenzhi Zhao
5. Shihong Du
6. Bo Du
7. Pedro García-Sevilla
8. Mathieu Fauvel

In Media Technology

1. Liangpei Zhang
2. Jon Atli Benediktsson
3. Shutao Li
4. Bo Du
5. Jocelyn Chanussot
6. Lefei Zhang
7. Wenzhi Zhao
8. Pedram Ghamisi

Citations in the corpus

• Earth system science related imaging spectroscopy — an assessment (2009, neutral): “Further, morphological approaches for joint exploitation of the spatial and spectral information available in the input data have been recently explored (Clark et al., 2003; Dell'Acqua et al., 2004; Plaza et al., 2005; Soille, 2003).”
• A Kernel-Based Feature Selection Method for SVM With RBF Kernel for Hyperspectral Image Classification (2014, background): “The extraction of morphological information has also been discussed [49], [50].”
• Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas (2009, background): “have proposed an extension to the morphological transforma tion in order to integrate spectral and spatial information from the hypers ctral data [25].”
• Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art (2016, background): “Some RS images, especially hyperspectral ones, contain hundreds of bands that can cause a small patch to be a really large data cube, which corresponds to a large number of neurons in a pretrained network [19], [20].”
• Denoising and Dimensionality Reduction Using Multilinear Tools for Hyperspectral Images (2008, background): “[3] proposes a multichannel mathematical morphology operator-based DR method, which incorporates image representation.”
• Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach (2016, methodology): “For evaluating the performance of the proposed SSFC, the classification results of SSFC are compared to other commonly used DR methods and spectral–spatial feature extraction methods, including PCA, LDA, LDE, RLDE, LFDA, NWFE, EMPs [32], and spectral+EMP (SEMP) [33].”
• Unsupervised Spectral–Spatial Feature Learning With Stacked Sparse Autoencoder for Hyperspectral Imagery Classification (2015, methodology): “including principle component analysis (PCA) [3] and linear discriminant analysis [4].”
• Clustering-Based Hyperspectral Band Selection Using Information Measures (2007, methodology): “In feature extraction, we would obtain a new and reduced data set representing the transformed initial information, whereas in feature selection, we would have a subset of relevant data from the original information.”
• Linear Versus Nonlinear PCA for the Classification of Hyperspectral Data Based on the Extended Morphological Profiles (2012, methodology): “Morphological profiles (MP), which combine spectral and spatial information, have been shown to be effective tools for classification of remote sensing data [1] [2] [3] [4] [5] [6].”