Exploiting manifold geometry in hyperspectral imagery
By Charles M. Bachmann, Thomas L. Ainsworth, Robert A. Fusina. IEEE Transactions on Geoscience and Remote Sensing, 2005
451 citations recorded by the source corpus.
Subfields: Ecology, Media Technology
View Paper: https://www.semanticscholar.org/paper/d7bd1659967cfe584e420b5e70109b5901072aa0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecology
- Xudong Kang
- Pedram Ghamisi
- Danfeng Hong
- Jocelyn Chanussot
- Jon Atli Benediktsson
- Xiuping Jia
- Bor-Chen Kuo
- Rob Heylen
In Media Technology
- Pedram Ghamisi
- Jon Atli Benediktsson
- Jocelyn Chanussot
- Danfeng Hong
- Xudong Kang
- Shutao Li
- Behnood Rasti
- Antonio J. Plaza
Citations in the corpus
- A Review of Nonlinear Hyperspectral Unmixing Methods (2014, background): “Many results have been published on nonlinear hyperspectral image classification [158]–[165], and manifold learning techniques have been used to deal with the nonlinearities encountered in bathymetric applications for determining the depth of shallow coastal waters [166].”
- Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox (2020, background): “Isometric mapping (ISOMAP) [44], [45] is a global geometric nonlinear feature extraction.”
- Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM (2014, unknown): “[34], [35] or extraction [?], [16], [36]–[38] techniques, i.”
- Feature Mining for Hyperspectral Image Classification (2013, methodology): “Spatial coherence, which was applied to image denoising and texture synthesis, was used to extend the locally linear embedding (LLE) and ISOMAP [123]–[125].”
- A new embedding quality assessment method for manifold learning (2011, background): “They also have many successful applications, such as motion detection [17], sample preprocessing [18], gait analysis [19], facial expression recognition [20], hyperspectral imagery processing [21], and visual tracking [22].”
- Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning (2014, unknown): “The potential value of manifold learning for HSI analysis has been demonstrated for applications including feature extraction [1], [11], segmentation [12], classification [13]–[15], anomaly detection [16], [17], and spectral unmixing [18]–[21] with some approaches exploiting interband correlation [1”