Spectral-Spatial Feature Fusion by Neural Network classification in remotely sensed hyperspectral images

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.

Miri Fatemeh sadat; Hosseini S. Abolfazl; Shaghaghi Kandovan Ramin

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

Journal Paper

Paper Information

Journal: SIGNAL AND DATA PROCESSING Year:2023 | Volume:20 | Issue:2 Page(s): 163-174

Download Full-Text

Persian Verion

View:

Download:

Cites:

Information Journal Paper

Title

Spectral-Spatial Feature Fusion by Neural Network classification in remotely sensed hyperspectral images

Author(s)

Miri Fatemeh sadat | Hosseini S. Abolfazl | Shaghaghi Kandovan Ramin | Issue Writer Certificate

Keywords

feature fusionQ4

spectralQ4

spatialQ4

neural networkQ4

SVMQ4

classificationQ4

Abstract

Hyper-spectral image classification is a popular topic in the field of remote sensing. Hyperspectral images (HSI) have rich spectral information and spatial information. Traditional hyperspectral image (HSI) classification methods typically use the spectral features and do not make full use of the spatial or other features of the HSI. In general, the classification approaches classify input data by considering the spectral information of the data to produce a classification map in order to discriminate different classes of interest. The pixel-wise classification approaches classify each pixel autonomously without considering information about spatial structures, further enhancement of classification results can be obtain by considering spatial dependences between pixels. However, how to fuse and utilize spectral-spatial features more efficiently is a challenging task. So the combination of spectral information and spatial information has become an effective means to obtain good classification results. Specifically, firstly, the principal component analysis (PCA) algorithm is used to extract the first principal component in the original hyperspectral image. Secondly, the residual network Gabor, GLCM and MP are introduced for each band to extract the spatial information of the image. Thirdly, the image is classified by using SVM to get the final classification result. In this paper, we have used the neural network classifier in the classification of hyperspectral images by integrating spectral and spatial properties in two methods stack and the method based on binary graphs. In spite of the traditional stack method, the use of local binary graph method to properly integrate spectral and spatial information is a desirable method for the simultaneous use of spectral information along with spatial information (feature fusion) in hyperspectral image classification. In each of these methods, the neural network classifier is applied to the spectral and spatial features separately and then compared with the performance of the support vector machine classifier in similar conditions. The classification results show that the proposed method can outperform other traditional classification techniques

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops