Improvement of Clustering for Hyperspectral Images using Spectral Information Divergence

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Ezzatabadi Pour H.

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Iranian Journal of Remote Sensing & GIS Year:2018 | Volume:10 | Issue:3 Page(s): 17-32

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

375

Download:

Cites:

Information Journal Paper

Title

Improvement of Clustering for Hyperspectral Images using Spectral Information Divergence

Author(s)

Ezzatabadi Pour H. | Issue Writer Certificate

Keywords

ClusteringQ2

Dissimilarity MeasureQ1

Spectral Information DivergenceQ1

Hyperspectral ImagesQ1

Abstract

K-Means is one of the most frequently used unsupervised classification approaches for remotely sensed image analysis. In standard K-Means version, the Euclidean distance (ED) has used to estimate the dissimilarity between an unknown vector data and the cluster center. Since, this measure is very sensitive to topographic and environmental effects on spectral observations, we have proposed to replace it with a new one for goal of hyperspectral image Clustering. The Spectral Information Divergence (SID) is a stochastic measure that is a more reliable Dissimilarity Measure when compared to ED as a deterministic measure. Where the ED measure the spectral distance between vector data and the clusters, SID models the probability distributions for vector data and clusters by normalizing their spectral signatures and measures the distances between them. This idea has applied to develop an enhanced Clustering framework. The experimental results on three real Hyperspectral Images collected by HyMap, HYDICE and Hyperion sensors show that the proposed method improves classification results. In the manner that the Kappa coefficient of the classification results of three hyperspectral imagery datasets increased by about 7%, 56% and 10%, respectively.

Cites

No record.

References

No record.

Cite

APA: Copy

Ezzatabadi Pour, H.. (2018). Improvement of Clustering for Hyperspectral Images using Spectral Information Divergence. REMOTE SENSING & GIS, 10(3 ), 17-32. SID. https://sid.ir/paper/358257/en

Vancouver: Copy

Ezzatabadi Pour H.. Improvement of Clustering for Hyperspectral Images using Spectral Information Divergence. REMOTE SENSING & GIS[Internet]. 2018;10(3 ):17-32. Available from: https://sid.ir/paper/358257/en

IEEE: Copy

H. Ezzatabadi Pour, “Improvement of Clustering for Hyperspectral Images using Spectral Information Divergence,” REMOTE SENSING & GIS, vol. 10, no. 3 , pp. 17–32, 2018, [Online]. Available: https://sid.ir/paper/358257/en

Related Journal Papers