Hyperspectral image classification using sub-band tensor factorization and convolutional neural network

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.

Mirzaei Sayeh; HAGHSHENAS JAVAD

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

Journal Paper

Paper Information

Journal: MACHINE VISION AND IMAGE PROCESSING Year:2020 | Volume:7 | Issue:1 Page(s): 123-133

Download Full-Text

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

Persian Verion

View:

596

Download:

Cites:

Information Journal Paper

Title

Hyperspectral image classification using sub-band tensor factorization and convolutional neural network

Author(s)

Mirzaei Sayeh | HAGHSHENAS JAVAD | Issue Writer Certificate

Keywords

Hyperspectral Image Classification

Sub-band Non-negative Tensor Factorization (NTF)

3D Convolutional Neural Network (3D CNN)

Abstract

In this paper, we are going to classify each pixel of a hyperspectral image. For this purpose, we group the spectral bands to sub-bands and try to decompose the corresponding sub-tensors to the endmember and abundance matrices. Abundance matrices obtained through tensor factorization methods contain spatial information in contrast to the ones acquired by matrix factorization. Therefore, the 2D abundance maps achieved by tensor decomposition methods, construct discriminant features for the classifier. A 3D CNN architecture is proposed for classification which utilizes the abundance maps of the individual sub-bands as input features. This way, we jointly exploit spectral and spatial information of the image. The experiments are performed on well-known hyperspectral data and reveal the effectiveness of the proposed sub-band tensor decomposition methods compared to matrix factorization approaches.

Cites

No record.

References

No record.

Cite

APA: Copy

Mirzaei, Sayeh, & HAGHSHENAS, JAVAD. (2020). Hyperspectral image classification using sub-band tensor factorization and convolutional neural network. MACHINE VISION AND IMAGE PROCESSING, 7(1 ), 123-133. SID. https://sid.ir/paper/394337/en

Vancouver: Copy

Mirzaei Sayeh, HAGHSHENAS JAVAD. Hyperspectral image classification using sub-band tensor factorization and convolutional neural network. MACHINE VISION AND IMAGE PROCESSING[Internet]. 2020;7(1 ):123-133. Available from: https://sid.ir/paper/394337/en

IEEE: Copy

Sayeh Mirzaei, and JAVAD HAGHSHENAS, “Hyperspectral image classification using sub-band tensor factorization and convolutional neural network,” MACHINE VISION AND IMAGE PROCESSING, vol. 7, no. 1 , pp. 123–133, 2020, [Online]. Available: https://sid.ir/paper/394337/en

Related Journal Papers