Iceberg detection and tracking using two-level feature extraction methodology on Antarctica Ocean

• Autorzy: Krishnan Rajakumar , Thangavelu Arunkumar , Panneer Prabhavathy , Devulapalli Sudheer , Misra Arundhati , Putrevu Deepak

Identyfikatory

DOI

10.1007/s11600-022-00917-1

• Języki publikacji: EN

Abstrakty

EN

This paper aims to develop an automatic feature extraction system for detecting icebergs in Antarctica. Extracting suitable features to discriminate an iceberg from sea ice and land melting based on its content is tedious. Especially in Synthetic Aperture Radar data, high image content is highly affected by speckle noise. Establishing the appropriate spatial relationship between pixels is not producing much accuracy with the standard low-level features. The proposed method introduces the two-level iceberg detection and tracking algorithm. The available samples were used to train the first-level convolution neural network-based features. False-positive predictions have been removed using the multiscale contourlet-based Haralick texture features in the second level. The final detected iceberg movement has been tracked using the temporal image data. The distance moved in both temporal images is computed with the help of latitude and longitude information. The proposed methodology exhibited the best performance over state-of-the-art methods and acquired 79.1% precision and 83.8 F1 score.

Słowa kluczowe

EN

iceberg detection; image retrieval; feature extraction; texture features; CNN; CBIR; contourlet

PL

wykrywanie góry lodowej; pobieranie obrazu; ekstrakcja cech; cechy tekstury

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2022
• Tom: Vol. 70, no 6
• Strony: 2953--2963
• Opis fizyczny: Bibliogr. 31 poz.

Twórcy

autor

Krishnan Rajakumar

• School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu 632014, India

autor

Thangavelu Arunkumar

• School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu 632014, India

autor

Panneer Prabhavathy

• School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu 632014, India

autor

Devulapalli Sudheer

• School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu 632014, India

• https://orcid.org/0000-0001-8780-210X

autor

Misra Arundhati

• Space Application Centre, Ahmadabad 380015, India

autor

Putrevu Deepak

• Space Application Centre, ISRO, Ahmadabad 380015, India

Bibliografia

• 1. Albregtsen F (2008) Statistical texture measures computed from gray level coocurrence matrices. Image Processing Laboratory, Department of Informatics, University of Oslo 5(5)
• 2. Bamberger RH, Smith MJT (1992) A filter bank for the directional decomposition of images: theory and design. IEEE Trans Signal Process 40(4):882–893. https://doi.org/10.1109/78.127960
• 3. Barbat MM et al (2019) An adaptive machine learning approach to improve automatic iceberg detection from SAR images. ISPRS J Photogramm Remote Sens 156:247–259
• 4. Barbat MM et al (2021) Automated iceberg tracking with a machine learning approach applied to SAR imagery: a Weddell sea case study. ISPRS J Photogramm Remote Sens 172:189–206
• 5. Burt PJ, Adelson EH (1987) The laplacian pyramid as a compact image code. Read Comput vis. https://doi.org/10.1109/TCOM.1983.1095851
• 6. Candes EJ, Demanet L (2005) The curvelet representation of wave propagators is optimally sparse. Commun Pure Appl Math 58(11):1472–1528
• 7. Champion I, Germain C, Da Costa JP, Alborini A, Dubois-Fernandez P (2013) Retrieval of forest stand age from SAR image texture for varying distance and orientation values of the gray level co occurrence matrix. IEEE Geosci Remote Sens Lett 11(1):5–9. https://doi.org/10.1109/LGRS.2013.2244060
• 8. Chibani Y (2005) Selective synthetic aperture radar and panchromatic image fusion by using the atrous wavelet decomposition. EURASIP J Adv in Signal Process 2005(14):1–8. https://doi.org/10.1155/ASP.2005.2207
• 9. Daubechies I (1988) Orthonormal bases of compactly supported wavelets. Commun Pure Appl Math 41(7):909–996
• 10. De Maesschalck R, Jouan-Rimbaud D, Massart DL (2000) The mahalanobis distance. Chemom Intell Lab Syst 50(1):1–18
• 11. Devulapalli S, Krishnan R (2019) Synthesized pansharpening using curvelet transform and adaptive neuro-fuzzy inference system. J Appl Remote Sens 13(3):034519. https://doi.org/10.1117/1.JRS.13.034519
• 12. Devulapalli S, Krishnan R (2021) Remote sensing image retrieval by integrating automated deep feature extraction and handcrafted features using curvelet transform. J Appl Remote Sens 15(1):016504
• 13. Ding B, Wen G, Huang X, Ma C, Yang X (2017) Target recognition in synthetic aperture radar images via matching of attributed scattering centers. IEEE J Sel Top Appl Earth Obs Remote Sens 10(7):3334–3347. https://doi.org/10.1109/JSTARS.2017.2671919
• 14. Do MN, Vetterli M (2005) The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans Image Process 14(12):2091–2106. https://doi.org/10.1109/TIP.2005.859376
• 15. Donoho DL (1998) Digital ridgelet transform via rectopolar coordinate transform. Stanf Univ Stanf CA Tech Rep 12:854–865
• 16. Fei GAO, Aidong LIU, Kai LIU, Erfu YANG, Hussain A (2019) A novel visual attention method for target detection from SAR images. Chin J Aeronaut 32(8):1946–1958. https://doi.org/10.1016/j.cja.2019.03.021
• 17. Filippidis A, Jain LC, Martin N (2000) Fusion of intelligent agents for the detection of aircraft in SAR images. IEEE Trans Pattern Anal Mach Intell 22(4):378–384. https://doi.org/10.1109/34.845380
• 18. Han D, Du Q, Aanstoos JV, Younan N (2015) Classification of levee slides from airborne synthetic aperture radar images with efficient spatial feature extraction. J Appl Remote Sens 9(1):097294. https://doi.org/10.1117/1.JRS.9.097294
• 19. Haralick RM (1971) On a texture-context feature extraction algorithm for remotely sensed imagery. In: 1971 IEEE conference on decision and control. IEEE
• 20. Harris A, Sluss JJ Jr, Refai HH, LoPresti PG (2006) Free-space optical wavelength diversity scheme for fog mitigation in a ground-to-unmanned-aerial-vehicle communications link. Opt Eng 45(8):086001. https://doi.org/10.1117/1.2338565
• 21. Li H-C, Celik T, Longbotham N, Emery WJ (2015) Gabor feature based unsupervised change detection of multitemporal SAR images based on two-level clustering. IEEE Geosci Remote Sens Lett 12(12):2458–2462. https://doi.org/10.1109/LGRS.2015.2484220
• 22. Molinier M, Laaksonen J, Rauste Y, Hame T (2007) Detecting changes in polarimetric SAR data with content-based image retrieval. In: 2007 IEEE international geoscience and remote sensing symposium. IEEE
• 23. Singh J, Mihai D (2012) Use of the second-kind statistics for VHR SAR image retrieval. In: 2012 9th international conference on communications (COMM). IEEE
• 24. Tang Xu, Jiao L (2016) Fusion similarity-based reranking for SAR image retrieval. IEEE Geosci Remote Sens Lett 14(2):242–246. https://doi.org/10.1109/LGRS.2016.2636819
• 25. Tang Xu, Jiao L, Emery WJ (2017) SAR image content retrieval based on fuzzy similarity and relevance feedback. IEEE J Sel Top Appl Earth Obs Remote Sens 10(5):1824–1842. https://doi.org/10.1109/JSTARS.2017.2664119
• 26. Topouzelis KN (2008) Oil spill detection by SAR images: dark formation detection, feature extraction and classification algorithms. Sensors 8(10):6642–6665. https://doi.org/10.3390/s8106642
• 27. Tupin F, Maitre H, Mangin JF, Nicolas JM, Pechersky E (1998) Detection of linear features in SAR images: application to road network extraction. IEEE Trans Geosci Remote Sens 36(2):434–453. https://doi.org/10.1109/36.662728
• 28. Vachon PW, Dobson FW (2000) Wind retrieval from RADARSAT SAR images: selection of a suitable C-band HH polarization wind retrieval model. Can J Remote Sens 26(4):306–313. https://doi.org/10.1080/07038992.2000.10874781
• 29. Yang X, Ding J (2020) A computational framework for iceberg and ship discrimination: case study on Kaggle competition. IEEE Access 8:82320–82327
• 30. Ye F, Luo W, Dong M, He H, Min W (2019) SAR image retrieval based on unsupervised domain adaptation and clustering. IEEE Geosci Remote Sens Lett 16(9):1482–1486. https://doi.org/10.1109/LGRS.2019.2896948
• 31. Zhou GY, Cui Yi, Chen YL, Yang J, Rashvand HF (2010) SAR image edge detection using curvelet transform and Duda operator. Electron Lett 46(2):167–169. https://doi.org/10.1049/el.2010.2888

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).