Ms. Linlin Miao Profile

Linlin Miao

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 23 February 2023 Paper

Hyperspectral image classification based on multi-feature fusion and different dimensionality reduction methods

Linlin Miao, Shiqing Dou, Yong Xu

Proceedings Volume 12551, 1255117 (2023) https://doi.org/10.1117/12.2668169

KEYWORDS: Image classification, Hyperspectral imaging, Principal component analysis, Feature extraction, Feature selection, Feature fusion, Support vector machines, Data fusion

Read Abstract +

The rich band information of hyperspectral images provides data support for accurate classification, but the information redundancy also brings bad effects to image classification. In order to improve the classification accuracy, this paper takes Liutang Town of Guilin City, Guangxi Province as the research area, and "OVS-1A/B" hyperspectral image as the data source. Based on two different dimensionality reduction methods, Principal Components Analysis (PCA) and CfsSubsetEval GreedyStepwise (CG), combined with Support Vector Machine (SVM), classifiers are used to classify hyperspectral images based on four multi-feature fusion schemes, and their classification accuracy is compared. The results show that: (1) In the two classification schemes with different dimensionality reduction methods, the classification accuracy of ground objects increases continuously with the increase of the types of fused features. (2) In the same combination scheme, the classification accuracy obtained by CG feature selection is higher than that obtained by PCA feature extraction. (3) The classification accuracy of scheme 4 using CG method for feature selection was the highest, the overall classification accuracy was 97.12%, and the Kappa coefficient was 0.9556.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years