Classification of Detected Changes From Multitemporal High-Res Xband SAR Images: Intensity and Texture Descriptors From SuperPixels

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 9, Issue 12, 2016, Pages 5436-5448

  Barreto, Thiago L M ^a   Rosa, Rafael A S ^a   Wimmer, Christian ^a   Moreira, João R ^a   Bins, Leonardo S ^b   Cappabianco, Fábio Augusto Menocci ^c   Almeida, Jurandy ^c  

^a Bradar Indústria ^*  (Brazil)

^b INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS   (Brazil)

^c FEDERAL UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

Change detection; classification; multilayer perceptron (MLP); object correlation images (OCIs); remote sensing; simple linear iterative clustering (SLIC); superpixel; synthetic aperture radar (SAR) images

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLUSTERING ALGORITHMS; DEFORESTATION; FEATURE EXTRACTION; IMAGE RECONSTRUCTION; IMAGE SEGMENTATION; ITERATIVE METHODS; MULTILAYERS; PIXELS; RADAR; REMOTE SENSING; SIGNAL DETECTION; SUPERVISED LEARNING; SYNTHETIC APERTURE RADAR;

CHANGE DETECTION; CORRELATION IMAGES; ITERATIVE CLUSTERING; MULTI LAYER PERCEPTRON; SUPER PIXELS; SYNTHETIC APERTURE RADAR (SAR) IMAGES;

RADAR IMAGING;

AIRBORNE SENSOR; ALGORITHM; DEFORESTATION; IMAGE CLASSIFICATION; PIXEL; REMOTE SENSING; SUPERVISED LEARNING; SYNTHETIC APERTURE RADAR;

EID: 85006129580     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2016.2621818     Document Type: Article

References (57)

1. B. Jongman, P. J. Ward, and J. C. Aerts, "Global exposure to river and coastal flooding: Long term trends and changes," Global Environ. Change, vol. 22, no. 4, pp. 823-835, 2012.
2. M. Campanili and W. B. Schäffer, Mata Atlântica: Manual de adequação ambiental, Ministério do Meio Ambiente, Braslia, Brazil, 2010.
3. S. Vinod, "Deforestation and water pollution impact on mosquitoes related epidemic diseases in Nanded region," Biosci. Discovery, vol. 2, pp. 309-316, 2011.
4. A. Roth et al., "Concurrent outbreaks of dengue, chikungunya and Zika virus infections-an unprecedented epidemic wave of mosquito-borne viruses in the pacific 2012-2014," Euro Surveillance, vol. 19, no. 41, pp. 1-8, 2014.
5. F. Wang et al., "Unsupervised change detection on SAR images using triplet Markov field model," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 4, pp. 697-701, Jul. 2013.
6. Y. Ban and O. A. Yousif, "Multitemporal spaceborne SAR data for urban change detection in China," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 4, pp. 1087-1094, Aug. 2012.
7. M. J. Canty, Image Analysis, Classification and Change Detection in Remote sensing: With Algorithms for ENVI/IDL and Python. Boca Raton, FL, USA: CRC Press, 2014.
8. G. Quin, B. Pinel-Puyssegur, J.-M. Nicolas, and P. Loreaux, "Mimosa: An automatic change detection method for SAR time series," IEEE Trans. Geosci. Sens., vol. 52, no. 9, pp. 5349-5363, Sep. 2014.
9. J. Zheng and H. You, "A new model-independent method for change detection in multitemporal SAR images based on radon transform and Jeffrey divergence," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 1, pp. 91-95, Jan. 2013.
10. B. Aiazzi, L. Alparone, S. Baronti, A. Garzelli, and C. Zoppetti, "Nonparametric change detection in multitemporal sar images based on meanshift clustering," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 4, pp. 2022-2031, Apr. 2013.
11. T. L. M. Barreto, J. Almeida, and F. A. M. Cappabianco, "Estimating accurate water levels for rivers and reservoirs by using SAR products: A multitemporal analysis," Pattern Recog. Lett., vol. 83, part 2, pp. 224-233, Nov. 2016, doi: 10.1016/j.patrec.2016.05.015.
12. K. A. de Macedo and C. Wimmer, "Time series of airborne DInSAR data over the amazon flooded vegetation: Water level changes," in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2015, pp. 5252-5255.
13. K. A. C. De Macedo et al., "Long-term airborne DInSAR measurements at X-and P-bands: A case study on the application of surveying geohazard threats to pipelines," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 3, pp. 990-1005, Jun. 2012.
14. R. A. Rosa, D. Fernandes, J. B. Nogueira, and C. Wimmer, "Automatic change detection inmultitemporal X-and P-band SAR images using Gram- Schmidt process," in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2015, pp. 2797-2800.
15. X. Chen, J. Chen, Y. Shi, and Y. Yamaguchi, "An automated approach for updating land cover maps based on integrated change detection and classification methods," ISPRS J. Photogrammetry Remote Sens., vol. 71, pp. 86-95, 2012.
16. T. L. Barreto et al., "Deforestation change detection using high-resolution multi-temporal Xband SAR images and supervised learning classification," in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2016, pp. 5201-5204.
17. M. Hussain, D. Chen, A. Cheng, H. Wei, and D. Stanley, "Change detection from remotely sensed images: From pixel-based to object-based approaches," ISPRS J. Photogrammetry Remote Sens., vol. 80, pp. 91-106, 2013.
18. P. L. Rosin, "Thresholding for change detection," Comput. Vis. Image Understanding, vol. 86, no. 2, pp. 79-95, 2002.
19. X. Su, C.-A. Deledalle, F. Tupin, and H. Sun, "NORCAMA: Change analysis in SAR time series by likelihood ratio change matrix clustering," ISPRS J. Photogrammetry Remote Sens., vol. 101, pp. 247-261, 2015.
20. O. Yousif, "Urban change detection using multitemporal SAR images," Ph.D. dissertation, School of Architecture and the Built Environment, Urban Planning and Environment, Geoinformatics, KTH Royal Institute of Technology, Stockholm, Sweden, Jun. 2015.
21. G. Chen, G. J. Hay, L. M. Carvalho, and M. A. Wulder, "Object-based change detection," Int. J. Remote Sens., vol. 33, no. 14, pp. 4434-4457, 2012.
22. M. A. Vieira et al., "Object based image analysis and data mining applied to a remotely sensed landsat time-series to map sugarcane over large areas," Remote Sens. Environ., vol. 123, pp. 553-562, 2012.
23. R. Rasi et al., "Automatic updating of an object-based tropical forest cover classification and change assessment," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 1, pp. 66-73, Feb. 2013.
24. W. R. Nascimento, P. W. M. Souza-Filho, C. Proisy, R. M. Lucas, and A. Rosenqvist, "Mapping changes in the largest continuous amazonian mangrove belt using object-based classification of multisensor satellite imagery," Estuarine, Coastal Shelf Sci., vol. 117, pp. 83-93, 2013.
25. N.-T. Son, C.-F. Chen, N.-B. Chang, C.-R. Chen, L.-Y. Chang, and B.-X. Thanh, "Mangrove mapping and change detection in Ca MAU peninsula, vietnam, using landsat data and object-based image analysis," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 2, pp. 503-510, Feb. 2015.
26. R. Qin, X. Huang, A. Gruen, and G. Schmitt, "Object-based 3-D building change detection on multitemporal stereo images," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 5, pp. 2125-2137, May 2015.
27. L. Ma et al., "Object-based change detection in urban areas: The effects of segmentation strategy, scale, and feature space on unsupervised methods," Remote Sens., vol. 8, no. 9, pp. 1-18, 2016.
28. U. C. Benz, P. Hofmann, G. Willhauck, I. Lingenfelder, and M. Heynen, "Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information," ISPRS J. Photogrammetry Remote Sens., vol. 58, no. 3, pp. 239-258, 2004.
29. F. Qin, J. Guo, and F. Lang, "Superpixel segmentation for polarimetric SAR imagery using local iterative clustering," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 1, pp. 13-17, Jan. 2015.
30. M. Baatz and A. Schäpe, "Multiresolution segmentation: An optimization approach for high quality multi-scale image segmentation," Angewandte Geographische Informationsverarbeitung XII, vol. 58, pp. 12-23, 2000.
31. Y. Liu, D. Zhang, G. Lu, and W.-Y. Ma, "A survey of content-based image retrieval with high-level semantics," Pattern Recog., vol. 40, no. 1, pp. 262-282, 2007.
32. C. O. Dumitru and M. Datcu, "Information content of very high resolution SAR images: Study of feature extraction and imaging parameters," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 8, pp. 4591-4610, Aug. 2013.
33. G. Mountrakis, J. Im, and C. Ogole, "Support vector machines in remote sensing:Areview," ISPRS J. Photogrammetry Remote Sens., vol. 66, no. 3, pp. 247-259, 2011.
34. V. Dey, Y. Zhang, and M. Zhong, "A review on image segmentation techniqueswith remote sensing perspective," in Proc. ISPRS Tech. Commission 7th Symp. 100 Years, 2010, pp. 31-42.
35. A. Ghosh, N. S. Mishra, and S. Ghosh, "Fuzzy clustering algorithms for unsupervised change detection in remote sensing images," Inf. Sci., vol. 181, no. 4, pp. 699-715, 2011.
36. T. Celik, "A Bayesian approach to unsupervised multiscale change detection in synthetic aperture radar images," Signal Process., vol. 90, no. 5, pp. 1471-1485, 2010.
37. A. Filippi, A.G. Klein, I. Dobreva, and J. R. Jensen, "Self-organizingmapbased applications in remote sensing," in Self-Organizing Maps. Rijeka, Croatia: INTECH, 2010.
38. H. Wan, C. Jung, B. Hou, G. Wang, and Q. Tang, "Novel change detection in SAR imagery using local connectivity," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 1, pp. 174-178, Jan. 2013.
39. Y. Bazi, S. Member, L. Bruzzone, S. Member, and F. Melgani, "An unsupervised approach based on the generalized gaussian model to automatic change detection in multitemporal SAR images," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 4, pp. 874-887, Apr. 2005.
40. F. Bovolo, C. Marin, and L. Bruzzone, "A hierarchical approach to change detection in very high resolution SAR images for surveillance applications," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 4, pp. 2042-2054, Apr. 2013.
41. L. M. Rocha, F. A. Cappabianco, and A. X. Falcão, "Data clustering as an optimum-path forest problem with applications in image analysis," Int. J. Imag. Syst. Technol., vol. 19, no. 2, pp. 50-68, 2009.
42. J. P. Papa, A. X. Falcao, and C. T. Suzuki, "Supervised pattern classification based on optimum-path forest," Int. J. Imaging Syst. Technol., vol. 19, no. 2, pp. 120-131, 2009.
43. R. Achanta et al., "SLIC superpixels compared to state-of-the-art superpixel methods," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 11, pp. 2274-2282, Nov. 2012.
44. J. Im and J. R. Jensen, "A change detection model based on neighborhood correlation image analysis and decision tree classification," Remote Sens. Environ., vol. 99, no. 3, pp. 326-340, 2005.
45. J. Im, J. Jensen, and J. Tullis, "Object-based change detection using correlation image analysis and image segmentation," Int. J. Remote Sens., vol. 29, no. 2, pp. 399-423, 2008.
46. R. M. Haralick, K. Shanmugam, and I. H. Dinstein, "Textural features for image classification," IEEE Trans. Syst. Man Cybern., vol. SMC-3, no. 6, pp. 610-621, Nov. 1973.
47. F. T. Ulaby, F. Kouyate, B. Brisco, and T. L. Williams, "Textural information in SAR images," IEEE Trans. Geosci. Remote Sens., vol. GE-24, no. 2, pp. 235-245, Mar. 1986.
48. D. R. G. H. R. Williams and G. Hinton, "Learning representations by back-propagating errors," Nature, vol. 323, pp. 533-536, 1986.
49. M. Hall et al., "The WEKA data mining software: An update," ACM SIGKDD Explorations Newslett., vol. 11, no. 1, pp. 10-18, 2009.
50. C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, pp. 27-1-27-27, 2011. [Online]. Available: http://www.csie.ntu.edu.tw/cjlin/libsvm
51. E. R. Garg and E.A. Kumar, "Comparison of various noise removals using Bayesian framework," Int. J. Modern Eng. Res., vol. 2, no. 1, pp. 265-270, 2012.
52. N. Baghdadi et al., "Signal level comparison between terrasar-x and cosmo-skymed SAR sensors," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 3, pp. 448-452, Mar. 2015.
53. T. P. Weldon, W. E. Higgins, and D. F. Dunn, "Efficient Gabor filter design for texture segmentation," Pattern Recog., vol. 29, no. 12, pp. 2005-2015, Dec. 1996.
54. J. Almeida, N. J. Leite, and R. S. Torres, "Comparison of video sequences with histograms of motion patterns," in Proc. IEEE Int. Conf. Image Process., 2011, pp. 3673-3676.
55. J. Almeida, J. A. Santos, B. Alberton, L. P. C. Morellato, and R. S. Torres, "Phenological visual rhythms: Compact representations for fine-grained plant species identification," Pattern Recog. Lett., vol. 81, pp. 90-100, Oct. 2016, doi: 10.1016/j.patrec.2015.11.028
56. J. Almeida et al., "Deriving vegetation indices for phenology analysis using genetic programming," Ecological Informat., vol. 26, pp. 61-69, 2015.
57. F. A. Faria, J. Almeida, B. Alberton, L. P. C. Morellato, and R. S. Torres, "Fusion of time series representations for plant recognition in phenology studies," Pattern Recog. Lett., vol. 83, part 2, pp. 205-214, Nov. 2016, doi: 10.1016/j.patrec.2016.03.005