The Accuracy Improvement of the Hyperspectral Satellite Image Classification by Using the Development of a Convolutional Neural Network and Deep Learning

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.

MAHMOUDI SOMAYEH; Neisani Samani Najmeh; TOOMANIAN ARA

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

Journal Paper

Paper Information

Journal: ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY Year:2022 | Volume:9 | Issue:4 Page(s): 109-125

Download Full-Text

Persian Verion

View:

175

Download:

Cites:

Information Journal Paper

Title

The Accuracy Improvement of the Hyperspectral Satellite Image Classification by Using the Development of a Convolutional Neural Network and Deep Learning

Author(s)

MAHMOUDI SOMAYEH | Neisani Samani Najmeh | TOOMANIAN ARA | Issue Writer Certificate

Keywords

Hyperspectral Images

Classification

Deep Learning

Convolutional Neural Network

Abstract

Nowadays, with the advancement of technology, numerous sensors provide high spatial and spectral resolution images. So far, several methods have been proposed for hyperspectral image Classification, each of which seeks to overcome a number of computational and processing challenges of hyperspectral data. The efficiency of multi-layer perceptron neural networks is greatly reduced due to the increase in the number of parameters along with the increase of the layers, which is essential in complex topics such as hyperspectral image Classification. In recent years, the concept of Deep Learning, especially Convolutional Neural Networks (CNN), has attracted the attention of pattern recognition researchers due to the automatic generation of features and the reduction of parameters compared to the multi-layer perceptron neural networks by sharing the parameters in each layer. The goal of the present study is to develop a Convolutional Neural Networks (CNN in order to classify Hyperspectral Images. The innovation of this study is to provide a framework to use Deep Learning. The proposed framework includes four steps. The first step is to reduce dimension by using the sub-space method, the second step is to prepare the CNN inputs, the third step is to augment the teaching data, and the fourth step is to design the CNN architecture. Implementation of the proposed framework on the benchmark data of the University of Pavia, despite the use of a limited number of educational data, led to the Classification accuracy of 98. 3%.

Cites

No record.

References

No record.

Cite

APA: Copy

MAHMOUDI, SOMAYEH, Neisani Samani, Najmeh, & TOOMANIAN, ARA. (2022). The Accuracy Improvement of the Hyperspectral Satellite Image Classification by Using the Development of a Convolutional Neural Network and Deep Learning. ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY, 9(4 ), 109-125. SID. https://sid.ir/paper/1051243/en

Vancouver: Copy

MAHMOUDI SOMAYEH, Neisani Samani Najmeh, TOOMANIAN ARA. The Accuracy Improvement of the Hyperspectral Satellite Image Classification by Using the Development of a Convolutional Neural Network and Deep Learning. ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY[Internet]. 2022;9(4 ):109-125. Available from: https://sid.ir/paper/1051243/en

IEEE: Copy

SOMAYEH MAHMOUDI, Najmeh Neisani Samani, and ARA TOOMANIAN, “The Accuracy Improvement of the Hyperspectral Satellite Image Classification by Using the Development of a Convolutional Neural Network and Deep Learning,” ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY, vol. 9, no. 4 , pp. 109–125, 2022, [Online]. Available: https://sid.ir/paper/1051243/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops