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As Earth observation technology has advanced, the volume of remote sensing big data has grown rapidly, offering significant obstacles to efficient and effective processing and analysis. A convolutional neural network refers to a neural network that covers convolutional calculations. It is a form of deep learning, and convolutional neural networks have characterization learning characteristics, which can classify information into different data. Remote Sensing Data Processing from various sensors has been attracting with more information in Remote Sensing. Remote sensing data is generally adjusted and refined through image processing. Image processing techniques, such as filtering and feature detection, are ideal for dealing with the high-dimensionality of geographically distributed systems. The geological entity is a term in geological work which refers to the product of geological processes that occupy a certain space in the Earth’s crust and are different from other materials. They are of different sizes and are divided into different types according to their size. It mainly focuses on improving classification accuracy and accurately describing scattering types. For geological entity recognition, this paper proposed a Deep Convolutional Neural Network Polarized Synthetic Aperture Radar (DCNN-PSAR). It is expected to use deep convolutional neural network technology and polarized SAR technology to explore new methods of geological entities and improve geological recognition capabilities. With the help of Multimodal Remote Sensing Data Processing, it is now possible to characterize and identify the composition of the Earth’s surface from orbital and aerial platforms. This paper proposes a ground object classification algorithm for polarized SAR images based on a fully convolutional network, which realizes the geological classification function and overcomes the shortcomings of too long. The evaluation of DCNN-PSAR shows that the accuracy of the water area is showing a rising trend, and the growth rate is relatively fast in the early stage, which directly changes from 0.14 to 0.6. Still, the increase is slower in the later stage. DCNN-PSAR achieves the highest quality of remote sensing data extraction.
Wydawca
Czasopismo
Rocznik
Tom
Strony
1565--1577
Opis fizyczny
Bibliogr. 31 poz., rys., tab.
Twórcy
autor
- School of Prospecting & Surveying Engineering, Changchun Institute of Technology, Changchun 130021, Jinlin, China
- Jilin Provincial Key Laboratory of Changbai Historical Culture and VR Reconstruction Technology, Changchun 130021, Jinlin, China
autor
- Qingdao Institute of Marine Geology, CGS, Qingdao 266071, Shandong, China
- Laboratory for Marine Mineral Resources, Qingdao NationalLaboratory for Marine Science and Technology, Qingdao 266071, Shandong, China
autor
- School of Prospecting & Surveying Engineering, Changchun Institute of Technology, Changchun 130021, Jinlin, China
- Jilin Provincial Key Laboratory of Changbai Historical Culture and VR Reconstruction Technology, Changchun 130021, Jinlin, China
autor
- School of Prospecting & Surveying Engineering, Changchun Institute of Technology, Changchun 130021, Jinlin, China
Bibliografia
- 1. Abo El-Hassan M, Awadalla KH, Hussein KF (2020) Shaped-beam circularly polarized antenna array of linear elements for satellite and SAR applications. Wirel Pers Commun 110(2):605–619
- 2. Acharya UR, Fujita H, Lih OS, Adam M, Tan JH, Chua CK (2017) Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network. Knowl-Based Syst 132:62–71
- 3. Adriano B, Yokoya N, Xia J, Miura H, Liu W, Matsuoka M, Koshimura S (2021) Learning from multimodal and multitemporal earth observation data for building damage mapping. ISPRS J Photogramm Remote Sens 175:132–143
- 4. Anquez P, Pellerin J, Irakarama M, Cupillard P, Lévy B, Caumon G (2019) Automatic correction and simplification of geological maps and cross-sections for numerical simulations. CR Geosci 351(1):48–58
- 5. Chauhan S, Srivastava HS, Patel P (2019) Crop height estimation using RISAT-1 hybrid-polarized synthetic aperture radar data. IEEE J Sel Top Appl Earth Obs Remote Sens 12(8):2928–2933
- 6. Chen YH, Krishna T, Emer JS, Sze V (2016) Eyeriss: an energy-efficient reconfigurable accelerator for deep convolutional neural networks. IEEE J Solid-State Circuits 52(1):127–138
- 7. Chen H, Zhang Y, Kalra MK, Lin F, Chen Y, Liao P, Wang G (2017) Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans Med Imaging 36(12):2524–2535
- 8. El-Hassan MA, Hussein KFA, Farahat AE, Awadalla KH (2019) Shaped-beam circularly-polarized practical antenna array for land imaging SAR systems. Appl Comput Electromagn Soc J (ACES) 642–653
- 9. Hong D, Hu J, Yao J, Chanussot J, Zhu XX (2021) Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model. ISPRS J Photogramm Remote Sens 178:68–80
- 10. Hou Y, Li Z, Wang P, Li W (2018) Skeleton optical spectra-based action recognition using convolutional neural networks. IEEE Trans Circuits Syst Video Technol 28(3):807–811
- 11. https://www.kaggle.com/competitions/soilclassification/overview/2-submission
- 12. Ilerbaig J (2021) Archives as sediments: metaphors of deposition and archival thinking. Arch Sci 21(1):83–95
- 13. Kruthiventi SS, Ayush K, Babu RV (2017) Deepfix: a fully convolutional neural network for predicting human eye fixations. IEEE Trans Image Process 26(9):4446–4456
- 14. Li H, Perrie W (2016) Sea ice characterization and classification using hybrid polarimetry SAR. IEEE J Sel Top Appl Earth Obs Remote Sens 9(11):4998–5010
- 15. Li H, Mouche A, Wang H, Stopa JE, Chapron B (2019) Polarization dependence of azimuth cutoff from quad-pol SAR images. IEEE Trans Geosci Remote Sens 57(12):9878–9887
- 16. Liu M, Shi J, Li Z, Li C, Zhu J, Liu S (2017) Towards a better analysis of deep convolutional neural networks. IEEE Trans Visual Comput Graphics 23(1):91–100
- 17. Lu F, Wu F, Hu P, Peng Z, Kong D (2017) Automatic 3D liver location and segmentation via convolutional neural network and graph cut. Int J Comput Assist Radiol Surg 12(2):171–182
- 18. Mahdianpari M, Motagh M, Akbari V, Mohammadimanesh F, Salehi B (2019) A Gaussian random field model for de-speckling of multi-polarized synthetic aperture radar data. Adv Space Res 64(1):64–78
- 19. Manning PM (2018) The Hyde we live in: Stevenson, evolution, and the anthropogenic fog. Vic Lit Cult 46(1):181–199
- 20. McCann MT, Jin KH, Unser M (2017) Convolutional neural networks for inverse problems in imaging: a review. IEEE Signal Process Mag 34(6):85–95
- 21. Peng W, Cao Y, Shen C et al (2017) Temporal pyramid pooling based convolutional neural networks for action recognition. IEEE Trans Multimed 27(12):2613–2622
- 22. Schirrmeister RT, Springenberg JT, Fiederer LDJ, Glasstetter M, Eggensperger K, Tangermann M, Ball T (2017) Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp 38(11):5391–5420
- 23. Shen W, Zhou M, Yang F, Yu D, Dong D, Yang C, Tian J (2017) Multicrop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recogn 61:663–673
- 24. Spröhnle K, Fuchs EM, Pelizari PA (2017) Object-based analysis and fusion of optical and SAR satellite data for dwelling detection in refugee camps. IEEE J Sel Top Appl Earth Obs Remote Sens 10(5):1780–1791
- 25. Sun G, Kong Y, Jia X, Zhang A, Rong J, Ma H (2018) Synergistic use of optical and dual-polarized SAR data with multiple kernel learning for urban impervious surface mapping. IEEE J Sel Top Appl Earth Obs Remote Sens 12(1):223–236
- 26. Wang P, Cao Y, Shen C, Liu L, Shen HT (2016) Temporal pyramid pooling-based convolutional neural network for action recognition. IEEE Trans Circuits Syst Video Technol 27(12):2613–2622
- 27. Wang X, Shao Y, Tian W, Li K (2018) On the classification of mixed floating pollutants on the Yellow Sea of China by using a quad-polarized SAR image. Front Earth Sci 12(2):373–380
- 28. Ye X, Lin M, Zheng Q, Yuan X, Liang C, Zhang B, Zhang J (2019) A typhoon wind-field retrieval method for the dual-polarization SAR imagery. IEEE Geosci Remote Sens Lett 16(10):1511–1515
- 29. Yu K, Salzmann M (2017) Second-order convolutional neural networks. arXiv preprint arXiv:1703.06817
- 30. Zhang B, Mouche A, Lu Y, Perrie W, Zhang G, Wang H (2019) A geophysical model function for wind speed retrieval from C-band HH-polarized synthetic aperture radar. IEEE Geosci Remote Sens Lett 16(10):1521–1525
- 31. Zhang X, Zhao Y, Xie J, Li C, Hu Z (2020) Geological big data acquisition based on speech recognition. Multimed Tools Appl 79(33):24413–24428
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8481db03-67c9-4143-9abd-04ff0ca92868