Volume 20, Issue 2 (9-2023)
JSDP 2023, 20(2): 163-174 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Miri F, Hosseini S, Shaghaghi Kandovan R. Feature fusion by neural network classification in remotely sensed hyperspectral images. JSDP 2023; 20 (2) : 10
URL: http://jsdp.rcisp.ac.ir/article-1-1317-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1317-en.html
ECE Faculty
Abstract: (823 Views)
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
Article number: 10
Type of Study: Research |
Subject:
Paper
Received: 2022/06/12 | Accepted: 2023/07/17 | Published: 2023/10/22 | ePublished: 2023/10/22
Received: 2022/06/12 | Accepted: 2023/07/17 | Published: 2023/10/22 | ePublished: 2023/10/22
References
1. [1] M. Imani and H. Ghassemian, "Pansharpening optimisation using multiresolution analysis and sparse representation," International Journal of Image and Data Fusion, vol. 12, no. 4, pp. 1-23, 2017.
2. [2] M. Imani and H. Ghassemian, "Feature reduction of hyperspectral images Discriminant analysis and the first principal component," Journal of AI and Data Mining, vol. 3, no. 5, pp. 1-9, 2015. [DOI:10.5829/idosi.JAIDM.2015.03.01.01]
3. [3] M. Imani and H. Ghassemian, "Feature space discriminant analysis for hyperspectral data feature reduction," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 102, no. 5, pp. 1-13, 2015. [DOI:10.1016/j.isprsjprs.2014.12.024]
4. [4] A. Taghipour, H. Ghassemian, and F. Mirzapour, "Anomaly detection of hyperspectral imagery using differential morphological profile" in Electrical Engineering (ICEE), 2016 24th Iranian Conference on, 2016, vol. 12, no. 4, pp. 1219-1223, 2016. [DOI:10.1109/IranianCEE.2016.7585707]
5. [5] A. Taghipour and H. Ghassemian, "Hyperspectral Anomaly Detection Using Attribute Profiles" IEEE Geoscience and Remote Sensing Letters,vol. 12, no. 4, pp. 13-15, 2017.
6. [6] Z. Zhu, "Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications" ISPRS Journal of Photogrammetry and Remote Sensing, vol. 130, no. 13, pp. 370-384, 2017. [DOI:10.1016/j.isprsjprs.2017.06.013]
7. [7] Maryam Vafadar; Hassan Ghassemian, " Hyperspectral anomaly detection using Modified Principal component analysis reconstruction error" ,2017 Iranian Conference on Electrical Engineering (ICEE), DOI:10. 1109/Iranian CEE. 2017. 7985332. [DOI:10.1109/IranianCEE.2017.7985332]
8. [8] Hamid Nourollahi, S. Abolfazl Hosseini*, Ali Shahzadi & Ramin Shaghaghi Kandovan, Signal detection Using Rational Function Curve Fitting, Signal and Data Processing Journal Serial 54, Volume 19, Number 4 (3-2023), jsdp. rcisp. ac. ir
9. [9] R. Rajabi and H. Ghassemian, "Sparsity constrained graph regularized NMF for spectral unmixing of hyperspectral data," Journal of the Indian Society of Remote Sensing, vol. 43, no. 13, pp. 269-278, 2015. [DOI:10.1007/s12524-014-0408-2]
10. [10] F. Kowkabi, H. Ghassemian, and A. Keshavarz, "Hyperspectral endmember extraction and unmixing by a novel spatial-spectral preprocessing module," in Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International, vol. 12, no. 4, pp. 33823385, 2016. [DOI:10.1109/IGARSS.2016.7729874]
11. [11] Hongmin Gao; Zhonghao Chen ; Feng Xu " Adaptive spectral-spatial feature fusion network for hyperspectral image classification using limited training samples" ; International Journal of Applied Earth Observation and Geoinformation Volume 107, March 2022
12. [12] H. Ghassemian and D. A. Landgrebe, "object-oriented feature extraction method for image data compaction," IEEE Control Systems Magazine, vol. 8, no. 3, pp. 42 - 48, 1988. [DOI:10.1109/37.476]
13. [13] M. Fauvel, Y. Tarabalka and J. A. Benedi, "Advances in Spectral-Spatial Classification of Hyperspectral Images," Proceedings of the IEEE, vol. 101, no. 3, pp. 652 - 675, 2013. [DOI:10.1109/JPROC.2012.2197589]
14. [14] Mathieu FauvelMathieu FauvelJocelyn ChanussotJocelyn ChanussotJon Atli BenediktssonJon Atli BenediktssonJohannes R. SveinssonJohannes R. Sveinsson " Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles", "IEEE Transactions on Geoscience and Remote Sensing 46, 11 - part 2 (2008) 3804-3814" [DOI:10.1109/TGRS.2008.922034]
15. [15] Siyuan Hao, Yufeng Xia, Lijian Zhou, etc Yuanxin Ye; Wei Wang "Spectral and Spatial Feature Fusion for Hyperspectral Image Classification" Journals & Magazines, IEEE Geoscience and Remote Sensing Letters, Volume: 19,2022 [DOI:10.1109/LGRS.2022.3223090]
16. [16] W. Liao, M. Dalla Mura and J. Chanussot, "Fusion of Spectral and Spatial Information for Classification of Hyperspectral Remote-Sensed Imagery by Local Graph," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 2, pp. 583 - 594, 2016. [DOI:10.1109/JSTARS.2015.2498664]
17. [17] Maryam Imani, Hassan Ghassemian, "An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges" Elsevier Information Fusion 59 (2020) 59-83, 2020. [DOI:10.1016/j.inffus.2020.01.007]
18. [18] M. Imani and H. Ghassemian, "GLCM, Gabor, and morphology profiles fusion for hyperspectral image classification," 2016 24th Iranian Conference on Electrical Engineering (ICEE), pp. 460 - 465, 2016. [DOI:10.1109/IranianCEE.2016.7585566]
19. [19] Maryam Imani, Hassan Ghassemian, "An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges", journal Elsevier Information Fusion 59 (2020) 59-83 [DOI:10.1016/j.inffus.2020.01.007]
20. [20] Hosseini S Abolfazl, Beitollahi Mersedeh, "Using Savitsky-Golay filter and interval curve fitting in order to hyperspectral data compression", IEEE 2017. [DOI:10.1109/IranianCEE.2017.7985378]
21. [21] Debaleena Datta, Pradeep Kumar Mallick, Akash Kumar Bhoi, Muhammad Fazal Ijaz, Jana Shafi, and Jaeyoung Choi," Hyperspectral Image Classification: Potentials, Challenges, and Future Directions "Hindawi Computational Intelligence and Neuroscience Volume 2022. [DOI:10.1155/2022/3854635] [PMID] []
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
