Unsupervised spectral–spatial multiscale feature learning framework for hyperspectral image classification based on multiple kernel self-organizing maps-Reference-Cited by-同舟云学术

Unsupervised spectral–spatial multiscale feature learning framework for hyperspectral image classification based on multiple kernel self-organizing maps

Published:2020-10-23 Issue:04 Volume:14 Page:
ISSN:1931-3195
Container-title:Journal of Applied Remote Sensing
language:
Short-container-title:J. Appl. Rem. Sens.

Author:

Khattab Noha¹,Rashwan Shaheera¹,Ebied Hala M.²,Sheta Walaa¹,Shedeed Howida²,Tolba Mohamed F.²

Affiliation:

1. Informatics Research Institute, City of Scientific Research and Technological Applications, Alexandr

2. Ain Shams University, Faculty of Computer and Information Sciences, Cairo

Publisher

SPIE-Intl Soc Optical Eng

Subject

General Earth and Planetary Sciences

Reference52 articles.

1. Three decades of hyperspectral remote sensing of the Earth: A personal view

2. Recent advances in techniques for hyperspectral image processing

3. A Survey on Spectral–Spatial Classification Techniques Based on Attribute Profiles

4. Advances in Spectral-Spatial Classification of Hyperspectral Images

5. Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions

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