Classification of Hyperspectral Image using SVM Post-Processing for Shape Preserving Filter and PCA

α
Aditi Chandra
Aditi Chandra
σ
Narayan Panigrahi
Narayan Panigrahi
α Banasthali University

Send Message

To: Author

Classification of Hyperspectral Image using SVM Post-Processing for Shape Preserving Filter and PCA

Article Fingerprint

ReserarchID

CSTGV9TA34

Classification of Hyperspectral Image using SVM Post-Processing for Shape Preserving Filter and PCA Banner

AI TAKEAWAY

Connecting with the Eternal Ground
  • English
  • Afrikaans
  • Albanian
  • Amharic
  • Arabic
  • Armenian
  • Azerbaijani
  • Basque
  • Belarusian
  • Bengali
  • Bosnian
  • Bulgarian
  • Catalan
  • Cebuano
  • Chichewa
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Corsican
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Esperanto
  • Estonian
  • Filipino
  • Finnish
  • French
  • Frisian
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian Creole
  • Hausa
  • Hawaiian
  • Hebrew
  • Hindi
  • Hmong
  • Hungarian
  • Icelandic
  • Igbo
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Khmer
  • Korean
  • Kurdish (Kurmanji)
  • Kyrgyz
  • Lao
  • Latin
  • Latvian
  • Lithuanian
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Maltese
  • Maori
  • Marathi
  • Mongolian
  • Myanmar (Burmese)
  • Nepali
  • Norwegian
  • Pashto
  • Persian
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Samoan
  • Scots Gaelic
  • Serbian
  • Sesotho
  • Shona
  • Sindhi
  • Sinhala
  • Slovak
  • Slovenian
  • Somali
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tajik
  • Tamil
  • Telugu
  • Thai
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Welsh
  • Xhosa
  • Yiddish
  • Yoruba
  • Zulu

Abstract

This paper is based on an experimentation to preserve shapes of the natural classes in a hyperspectral image post classification of the image using SVM. The classifier classifies the vegetation types present in the hyperspectral image and then estimates the crop types present in the image. In doing so it preserves the spatial shapes of the vegetation types spread in the image using an Edge-preserving filter. The shape-preserving filter was applied prior to dimension reduction where by the low information content spectral components are discarded using Principal Component Analysis. The classification of the features is performed using SVM. The result has been found very effective in characterizing significant spectral and spatial structures of objects in a scene..

References

17 Cites in Article
  1. M Fauvel,Y Tarabalka,J Benediktsson,J Chanussot,J Tilton (2013). Advances in Spectral-Spatial Classification of Hyperspectral Images.
  2. N Panigrahi,B Mohan,G Athithan (2011). Classification of changed pixels in satellite images using Gaussian and Hessian functions.
  3. N Panigrahi (2009). Geographic Information Science.
  4. N Panigrahi (2014). Computations in Geographic Information System.
  5. Narayan Panigrahi,Meghavi Prashnani (2014). Impact Evaluation of Feature Reduction Techniques on Classification of Hyper Spectral Imagery.
  6. B Mojaradi,H Abrishami-Moghaddam,M Zoej,R Duin (2009). Dimensionality Reduction of Hyperspectral Data via Spectral Feature Extraction.
  7. Asmaa Hosni,Christoph Rhemann,Michael Bleyer,Carsten Rother,Margrit Gelautz (2013). Fast Cost-Volume Filtering for Visual Correspondence and Beyond.
  8. Zeev Farbman,Raanan Fattal,Dani Lischinski,Richard Szeliski (2008). Edge-preserving decompositions for multi-scale tone and detail manipulation.
  9. Xudong Kang,Shutao Li,Jon Benediktsson (2014). Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering.
  10. Junshi Xia,Lionel Bombrun,Tulay Adali,Yannick Berthoumieu,Christian Germain (2016). Spectral–Spatial Classification of Hyperspectral Images Using ICA and Edge-Preserving Filter via an Ensemble Strategy.
  11. B Pan,Z Shi,X Xu (2017). Hierarchical guidance filtering-based ensemble classification for hyperspectral images.
  12. Xudong Kang,Shutao Li,Jon Benediktsson (2014). Spectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering.
  13. Pedram Ghamisi,Mauro Dalla Mura,Jon Benediktsson (2015). A Survey on Spectral–Spatial Classification Techniques Based on Attribute Profiles.
  14. J Benediktsson,J Palmason,J Sveinsson (2005). Classification of hyperspectral data from urban areas based on extended morphological profiles.
  15. M Mura,A Villa,J Benediktsson,J Chanussot,L Bruzzone (2011). Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis.
  16. P Marpu,M Pedergnana,Mauro Dalla Mura,J Benediktsson,L Bruzzone (2013). Automatic Generation of Standard Deviation Attribute Profiles for Spectral–Spatial Classification of Remote Sensing Data.
  17. J Nalepa,M Kawulok (2019). Selecting training sets for support vector machines: a review.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Aditi Chandra. 2020. \u201cClassification of Hyperspectral Image using SVM Post-Processing for Shape Preserving Filter and PCA\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 20 (GJCST Volume 20 Issue F1): .

Download Citation

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
Classification
GJCST-F Classification: I.4.0
Version of record

v1.2

Issue date

August 25, 2020

Language
en
Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

Article Matrices
Total Views: 4545
Total Downloads: 1165
2026 Trends
Related Research

Published Article

This paper is based on an experimentation to preserve shapes of the natural classes in a hyperspectral image post classification of the image using SVM. The classifier classifies the vegetation types present in the hyperspectral image and then estimates the crop types present in the image. In doing so it preserves the spatial shapes of the vegetation types spread in the image using an Edge-preserving filter. The shape-preserving filter was applied prior to dimension reduction where by the low information content spectral components are discarded using Principal Component Analysis. The classification of the features is performed using SVM. The result has been found very effective in characterizing significant spectral and spatial structures of objects in a scene..

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]

Request Access

Please fill out the form below to request access to this research paper. Your request will be reviewed by the editorial or author team.
X

Quote and Order Details

Contact Person

Invoice Address

Notes or Comments

This is the heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

High-quality academic research articles on global topics and journals.

Classification of Hyperspectral Image using SVM Post-Processing for Shape Preserving Filter and PCA

Aditi Chandra
Aditi Chandra Banasthali University
Narayan Panigrahi
Narayan Panigrahi

Research Journals