Statistical Quantification of Texture Visual Features for Pattern Recognition by Analyzing Pre- and Post-Multispectral Landsat Satellite Imagery
Publication: Natural Hazards Review
Volume 22, Issue 4
Abstract
This paper investigates the performance of visual texture features based on the gray-level co-occurrence matrix to analyze multispectral remotely sensed Landsat images. Different case studies related to urbanization, flood, and drought are investigated in this research work. The changing land use/land cover pattern caused by urbanization, floods, and droughts is examined through quantitative assessment. Texture visual features, i.e., correlation, contrast, angular second moment or energy, and homogeneity, are derived from the gray-level co-occurrence matrix. These features are used to develop a pattern for the changing texture of land use/land cover. Human visual perception of smoothness and coarseness is related to the texture features and is later used to describe the texture features’ changing behavior. The quantitative assessment of texture features in terms of smoothness and coarseness establishes a novel pattern between pre- and postimages of urbanization, flood, and drought.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the submitted article. Information about the Journal’s data-sharing policy can be found here: https://ascelibrary.org/page/dataavailability.
Acknowledgments
The authors are thankful to all anonymous reviewers and the Editor-in-Chief for their comments, concerns, queries, and constructive suggestions. The authors also wish to express their sincere gratitude to the NASA Earth Observatory for the multispectral satellite data used in this research work and NASA Landsat Science for vital information on the Landsat satellite program.
References
Adam, V. 2006. “Washington, D.C. NASA Earth Observatory.” Accessed January 26, 2020. https://earthobservatory.nasa.gov/IOTD/view.php?id=6581.
Akter, S., and S. F. Wamba. 2019. “Big data and disaster management: A systematic review and agenda for future research.” Ann. Oper. Res. 283 (1): 939–959. https://doi.org/10.1007/s10479-017-2584-2.
Allen, J. 2005. “Great Flood of the Mississippi River, 1993.” Accessed April 9, 2020. https://earthobservatory.nasa.gov/IOTD/view.php?id=5422.
Almenar, J. B., A. Bolowich, T. Elliot, D. Geneletti, G. Sonnemann, and B. Rugani. 2019. “Assessing habitat loss, fragmentation, and ecological connectivity in Luxembourg to support spatial planning.” Landscape Urban Plann. 189 (1): 335–351. https://doi.org/10.1016/j.landurbplan.2019.05.004.
Arefin, R., S. G. Meshram, C. A. G. Santos, R. M. da Silva, and J. Pushparaj. 2020. “Hybrid modelling approach for water body change detection at Chalan Beel area in northern Bangladesh.” Environ. Earth Sci. 79 (19): 1–18. https://doi.org/10.1007/s12665-020-09185-y.
Chen, J., P. Sciusco, Z. Ouyang, R. Zhang, G. M. Henebry, R. John, and D. P. Roy. 2019. “Linear downscaling from MODIS to Landsat: Connecting landscape composition with ecosystem functions.” Landscape Ecol. 34 (12): 2917–2934. https://doi.org/10.1007/s10980-019-00928-2.
Conners, R. W., and C. A. Harlow. 1980. “A theoretical comparison of texture algorithms.” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-2 (3): 204–222. https://doi.org/10.1109/TPAMI.1980.4767008.
Coppin, P. R., and M. E. Bauer. 1996. “Digital change detection in forest ecosystems with remote sensing imagery.” Remote Sens. Rev. 13 (3–4): 207–234. https://doi.org/10.1080/02757259609532305.
Cui, Y., D. Cheng, C. E. Choi, W. Jin, Y. Lei, and J. S. Kargel. 2019. “The cost of rapid and haphazard urbanization: Lessons learned from the Freetown landslide disaster.” Landslides 16 (6): 1167–1176. https://doi.org/10.1007/s10346-019-01167-x.
Dalmiya, C. P., N. Santhi, and B. Sathyabama. 2019. “A novel feature descriptor for automatic change detection in remote sensing images.” Egypt. J. Remote Sens. Space. Sci. 22 (2): 183–192. https://doi.org/10.1016/j.ejrs.2018.03.005.
Dey, V., Y. Zhang, and M. Zhong. 2010. “A review on image segmentation techniques with remote sensing perspective.” In Proc., ISPRS TC VII Symp.: 100 Years ISPRS, edited by W. Wagner, and B. Székely, 31–41. Vienna, Austria: International Society for Photogrammetry and Remote Sensing.
Du, B., L. Ru, C. Wu, and L. Zhang. 2019. “Unsupervised deep slow feature analysis for change detection in multi-temporal remote sensing images.” IEEE Trans. Geosci. Remote Sens. 57 (12): 9976–9992. https://doi.org/10.1109/TGRS.2019.2930682.
Du, P., X. Wang, D. Chen, S. Liu, C. Lin, and Y. Meng. 2020. “An improved change detection approach using tri-temporal logic-verified change vector analysis.” ISPRS J. Photogramm. Remote Sens. 161 (Mar): 278–293. https://doi.org/10.1016/j.isprsjprs.2020.01.026.
Feng, H., C. W. Lim, L. Chen, X. Zhou, C. Zhou, and Y. Lin. 2014. “Sustainable deforestation evaluation model and system dynamics analysis.” Sci. World J. 2014 (Mar): 1–14. https://doi.org/10.1155/2014/106209.
Ferreira, M. P., F. H. Wagner, L. E. Aragão, Y. E. Shimabukuro, and C. R. de Souza Filho. 2019. “Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis.” ISPRS J. Photogramm. Remote Sens. 149 (1): 119–131. https://doi.org/10.1016/j.isprsjprs.2019.01.019.
Gong, M., Y. Yang, T. Zhan, X. Niu, and S. Li. 2019. “A generative discriminatory classified network for change detection in multispectral imagery.” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 12 (1): 321–333. https://doi.org/10.1109/JSTARS.2018.2887108.
Gotlieb, C. C., and H. E. Kreyszig. 1990. “Texture descriptors based on co-occurrence matrices.” Comput. Vision Graphics Image Process. 51 (1): 70–86. https://doi.org/10.1016/S0734-189X(05)80063-5.
Häme, T., L. Sirro, J. Kilpi, L. Seitsonen, K. Andersson, and T. Melkas. 2020. “A hierarchical clustering method for land cover change detection and identification.” Remote Sens. 12 (11): 1751. https://doi.org/10.3390/rs12111751.
Hansen, K. 2016. “Bolivia’s Lake Poopó disappears.” Accessed January 26, 2020. https://earthobservatory.nasa.gov/NaturalHazards/view.php?id=87363.
Haralick, R. M. 1979. “Statistical and structural approaches to texture.” Proc. IEEE 67 (5): 786–804. https://doi.org/10.1109/PROC.1979.11328.
Haralick, R. M., K. Shanmugam, and I. Dinstein. 1973. “Texture feature for image classification.” IEEE Trans. Syst. Man Cybern. 3 (6): 610–621. https://doi.org/10.1109/TSMC.1973.4309314.
Holli, R. 2014. “Growth of Sao Paulo, Brazil.” Accessed April 12, 2020. https://earthobservatory.nasa.gov/IOTD/view.php?id=83987.
Houston, J. B., B. Pfefferbaum, and C. E. Rosenholtz. 2012. “Disaster news: Framing and frame changing in coverage of major US natural disasters, 2000–2010.” Journalism Mass Commun. Q. 89 (4): 606–623. https://doi.org/10.1177/1077699012456022.
Howarth, P. J., and G. M. Wickware. 1981. “Procedures for change detection using Landsat digital data.” Int. J. Remote Sens. 2 (3): 277–291. https://doi.org/10.1080/01431168108948362.
Humeau-Heurtier, A. 2019. “Texture feature extraction methods: A survey.” IEEE Access 7: 8975–9000. https://doi.org/10.1109/ACCESS.2018.2890743.
Hussain, M., D. Chen, A. Cheng, H. Wei, and D. Stanley. 2013. “Change detection from remotely sensed images: From pixel-based to object-based approaches.” ISPRS J. Photogramm. Remote Sens. 80 (Jun): 91–106. https://doi.org/10.1016/j.isprsjprs.2013.03.006.
Im, J., H. Park, and W. Takeuchi. 2019. “Advances in remote sensing-based disaster monitoring and assessment.” Remote Sens. 11 (18): 1–4. https://doi.org/10.3390/rs11182181.
Kalaiselvi, S., and V. Gomathi. 2020. “α-cut induced fuzzy deep neural network for change detection of SAR images.” Appl. Soft Comput. 95 (Oct): 106510. https://doi.org/10.1016/j.asoc.2020.106510.
Kathryn, H. 2015. “Goose Lake dries up.” Accessed January 26, 2020. https://earthobservatory.nasa.gov/IOTD/view.php?id=86358.
Kotoky, P., M. Dutta, and G. Borah. 2012. “Changes in landuse and landcover along the Dhansiri River channel, Assam—A remote sensing and GIS approach.” J. Geol. Soc. India 79 (1): 61–68. https://doi.org/10.1007/s12594-012-0002-6.
Lilleand, T. M., R. W. Kiefer, and J. W. Chipman. 2015. Remote sensing and image interpretation. New York: Wiley.
Lindsey, R. 2000. “Shrinking Aral Sea.” Accessed January 26, 2020. https://earthobservatory.nasa.gov/Features/WorldOfChange/aral_sea.php.
Lloyd, K., P. L. Rosin, D. Marshall, and S. C. Moore. 2017. “Detecting violent and abnormal crowd activity using temporal analysis of gray level co-occurrence matrix (GLCM)-based texture measures.” In Vol. 28 of Machine vision and applications, 361–371. New York: Springer.
Lu, P., Y. Qin, Z. Li, A. C. Mondini, and N. Casagli. 2019. “Landslide mapping from multi-sensor data through improved change detection-based Markov random field.” Remote Sens. Environ. 231 (Sep): 111235. https://doi.org/10.1016/j.rse.2019.111235.
Lv, Z., T. Liu, C. Shi, J. A. Benediktsson, and H. Du. 2019. “Novel land cover change detection method based on K-means clustering and adaptive majority voting using bitemporal remote sensing images.” IEEE Access 7 (Jan): 34425–34437. https://doi.org/10.1109/ACCESS.2019.2892648.
Ma, W., Y. Xiong, Y. Wu, H. Yang, X. Zhang, and L. Jiao. 2019. “Change detection in remote sensing images based on image mapping and a deep capsule network.” Remote Sens. 11 (6): 626. https://doi.org/10.3390/rs11060626.
Massazza, A., C. R. Brewin, and H. Joffe. 2019. “The nature of ‘natural disasters’: Survivors’ explanations of earthquake damage.” Int. J. Disaster Risk Sci. 10 (3): 293–305. https://doi.org/10.1007/s13753-019-0223-z.
Numbisi, F. N., F. Van Coillie, and R. De Wulf. 2019. “Delineation of cocoa agroforests using multiseason Sentinel-1 SAR images: A low grey level range reduces uncertainties in GLCM texture-based mapping.” ISPRS Int. J. Geo-Inf. 8 (4): 179. https://doi.org/10.3390/ijgi8040179.
Nurda, N., R. Noguchi, and T. Ahamed. 2020. “Change detection and land suitability analysis for extension of potential forest areas in Indonesia using satellite remote sensing and GIS.” Forests 11 (4): 398. https://doi.org/10.3390/f11040398.
Ohana-Levi, N., T. Paz-Kagan, N. Panov, A. Peeters, A. Tsoar, and A. Karnieli. 2019. “Time series analysis of vegetation-cover response to environmental factors and residential development in a dryland region.” GISci. Remote Sens. 56 (3): 362–387. https://doi.org/10.1080/15481603.2018.1519093.
Przyborski, P., and R. Levy. 1999. “NASA Earth Observatory.” Accessed January 26, 2020. https://earthobservatory.nasa.gov/.
Psomiadis, E., K. X. Soulis, M. Zoka, and N. Dercas. 2019. “Synergistic approach of remote sensing and GIS techniques for flash-flood monitoring and damage assessment in Thessaly Plain Area, Greece.” Water 11 (3): 448. https://doi.org/10.3390/w11030448.
Qi, J., P. Yang, L. Newcombe, X. Peng, Y. Yang, and Z. Zhao. 2020. “An overview of data fusion techniques for Internet of Things enabled physical activity recognition and measure.” Inf. Fusion 55 (Mar): 269–280. https://doi.org/10.1016/j.inffus.2019.09.002.
Ramola, A., A. K. Shakya, and D. Van Pham. 2020. “Study of statistical methods for texture analysis and their modern evolutions.” Eng. Rep. 2 (4): e12149. https://doi.org/10.1002/eng2.12149.
Riebeek, H. 2011. “Floods in Cambodia.” Accessed April 9, 2020. https://earthobservatory.nasa.gov/NaturalHazards/view.php?id=76212.
Rokni, K., and T. A. Musa. 2019. “Normalized difference vegetation change index: A technique for detecting vegetation changes using Landsat imagery.” Catena 178 (Jul): 59–63. https://doi.org/10.1016/j.catena.2019.03.007.
Schipper, L., and M. Pelling. 2006. “Disaster risk, climate change and international development: Scope for, and challenges to, integration.” Disasters 30 (1): 19–38. https://doi.org/10.1111/j.1467-9523.2006.00304.x.
Scott, M. 2008. “Urbanization of Ouagadougou, Burkina Faso.” Accessed April 9, 2020. https://earthobservatory.nasa.gov/IOTD/view.php?id=8870.
Scott, M., and R. Lindsey. 2009. “Tehran urbanization.” Accessed April 9, 2020. https://earthobservatory.nasa.gov/?eocn=topnav&eoci=logo.
Shakya, A. K., R. Prakash, A. Ramola, and D. C. Pandey. 2017. “Change detection from pre- and posturbanisation LANDSAT 5 TM multispectral images.” In Proc., 2017 Int. Conf. on Innovations in Control, Communication and Information Systems (ICICCI), 1–6. New York: IEEE. https://doi.org/10.1109/ICICCIS.2017.8660870.
Shakya, A. K., A. Ramola, A. Kandwal, P. Mittal, and R. Prakash. 2019a. “Morphological change detection in terror camps of area 1 and 2 by pre-and post-strike through MOAB: A.” In Vol. 537 of Advances in communication, devices, and networking, edited by R. Bera, S. Sarkar, O. Singh, and H. Saikia, 253–263. Berlin: Springer. https://doi.org/10.1007/978-981-13-3450-4_29.
Shakya, A. K., A. Ramola, A. Kandwal, P. Mittal, and R. Prakash. 2019b. “Morphological change detection in terror camps of area 3 and 4 by pre-and post-strike through MOAB: B.” In Vol. 537 of Advances in communication, devices, and networking. Lecture notes in electrical engineering, edited by R. Bera, S. Sarkar, O. Singh, and H. Saikia, Berlin: Springer. https://doi.org/10.1007/978-981-13-3450-4_30.
Shakya, A. K., A. Ramola, A. Kandwal, and R. Prakash. 2018. “Comparison of supervised classification techniques with ALOS Palsar sensor for Roorkee region of Uttarakhand, India.” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 42 (5): 693–701. https://doi.org/10.5194/isprs-archives-XLII-5-693-2018.
Shakya, A. K., A. Ramola, and A. Vidyarthi. 2020. “Conversion of Landsat 8 multispectral data through modified private content-based image retrieval technique for secure transmission and privacy.” Eng. Rep. 2 (12): e12273. https://doi.org/10.1002/eng2.12273.
Shao, Z., H. Fu, D. Li, O. Altan, and T. Cheng. 2019. “Remote sensing monitoring of multi-scale watersheds impermeability for urban hydrological evaluation.” Remote Sens. Environ. 232 (1): 111338. https://doi.org/10.1016/j.rse.2019.111338.
Song, J., X. Tong, L. Wang, C. Zhao, and A. V. Prishchepov. 2019. “Monitoring finer-scale population density in urban functional zones: A remote sensing data fusion approach” Landscape Urban Plann. 190 (Oct): 103580. https://doi.org/10.1016/j.landurbplan.2019.05.011.
Stevens, J. 2015. “Losses in Lake Mead.” Accessed April 12, 2020. https://earthobservatory.nasa.gov/images/86426/losses-in-lake-mead.
Voiland, A. 2015. “Flooding in Iraq.” Accessed January 26, 2020. https://earthobservatory.nasa.gov/NaturalHazards/view.php?id=87011.
Wan, L., H. Zhang, G. Lin, and H. Lin. 2019. “A small-patched convolutional neural network for mangrove mapping at species level using high-resolution remote-sensing image.” Ann. GIS 25 (1): 45–55. https://doi.org/10.1080/19475683.2018.1564791.
Wang, C., Y. Zhang, and X. Wang. 2009. “Coarse-to-fine SAR image change detection method.” Remote Sens. Lett. 10 (12): 1153–1162. https://doi.org/10.1080/2150704X.2019.1663560.
Wu, J. C.-M., and Y.-C. Chen. 1992. “Statistical feature matrix for texture analysis.” CVGIP: Graphical Models Image Process. 54 (5): 407–419. https://doi.org/10.1016/1049-9652(92)90025-S.
Zhu, L., J. P. Walker, N. Ye, and C. Rüdiger. 2019. “Roughness and vegetation change detection: A pre-processing for soil moisture retrieval from multi-temporal SAR imagery.” Remote Sens. Environ. 225 (May): 93–106. https://doi.org/10.1016/j.rse.2019.02.027.
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Natural Hazards Review
Volume 22 • Issue 4 • November 2021
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© 2021 American Society of Civil Engineers.
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Received: Oct 6, 2020
Accepted: Apr 12, 2021
Published online: Jul 16, 2021
Published in print: Nov 1, 2021
Discussion open until: Dec 16, 2021
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