Abstract
This study focuses on evaluating the capability and contribution of using backscatter intensity image and textural bands from Sentinel-1A synthetic aperture radar (SAR) data for reducing the limitation of optical image classification and improving the classification accuracy. The study was carried out at Theni district of Tamil Nadu, India, which is characterized by very heterogeneous features. The optical multispectral images such as Linear imaging self scanning sensor-IV (LISS-IV), Sentinel-2A and Landsat8 were used. Support vector machine classifier performed on the different combination of SAR, optical image and texture features. Results showed that the optimal window size was 11 × 11, and mean and variance are optimal textural bands of gray-level co-occurrence matrix techniques. The best classification result was achieved with the combination of LISS-IV and Sentinel-1A-derived features (backscatter intensity and texture features) with an overall accuracy up to 78.49% and a kappa coefficient of up to 0.68, respectively. The combination of optical image and Sentinel-1A data decreased the spectral confusions between the classes, provided better classification results, and reasonably improved the accuracy.
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References
Amarsaikhan D, Blotevogel HH, van Genderen JL et al (2010) Fusing high-resolution SAR and optical imagery for improved urban land cover study and classification. Int J Image Data Fusion 1(1):83–97. https://doi.org/10.1080/19479830903562041
Asner GP (2001) Cloud cover in Landsat observations of the Brazilian Amazon. Int J Remote Sens 22(18):3855–3862
Brisco B, Brown RJ (1995) Multidate SAR/TM synergism for crop classification in Western Canada. Photogramm Eng Remote Sens 61(8):1009–1014
Cetin M (2015) Using GIS analysis to assess urban green space in terms of accessibility: case study in Kutahya. Int J Sustain Dev World Ecol 22(5):420–424
Cetin M (2016) Sustainability of urban coastal area management: a case study on Cide. J Sustain For 35(7):527–541
Cetin M, Adiguzel F, Kaya O, Sahap A (2018) Mapping of bioclimatic comfort for potential planning using GIS in Aydin. Environ Dev Sustain 20(1):361–375. https://doi.org/10.1007/s10668-016-9885-5
Franklin SE, Wulder MA, Gerylo GR (2001) Texture analysis of IKONOS panchromatic data for Douglas-fir forest age class separability in British Columbia. Int J Remote Sens 22(13):2627–2632. https://doi.org/10.1080/01431160120769
Fukuda S, Hirosawa H (2001) Support vector machine classification of land cover: application to polarimetric SAR data. IEEE Geosci Remote Sens Symp 1(5):187–189. https://doi.org/10.1109/IGARSS.2001.976097
Gao G (2010) Statistical modeling of SAR images: a survey. Sensors 10:775–795. https://doi.org/10.3390/s100100775
Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621. https://doi.org/10.1109/TSMC.1973.4309314
Heiselberg H (2016) A direct and fast methodology for ship recognition in sentinel-2 multispectral imagery. Remote Sens 8(12):1–11. https://doi.org/10.3390/rs8121033
Iyyappan M, Ramakrishnan SS, Srinivasa Raju K (2014) Study of discrimination between plantation and dense scrub based on backscattering behavior of C band SAR data. Int Arch Photogramm Remote Sens Spat Inf Sci (ISPRS Arch XL) 8:755–760. https://doi.org/10.5194/isprsarchives-xl-8-755-2014
Joshi N, Baumann M, Ehammer A, Fensholt R, Grogan K, Hostert P, Jepsen MR, Kuemmerle T, Meyfroidt P, Mitchard ET, Reiche J (2016) A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sens 8(1):70
Kaya E, Agca M, Adiguzel F, Cetin M (2018) Spatial data analysis with R programming for environment. Hum Ecol Risk Assess Int J. https://doi.org/10.1080/10807039.2018.1470896
Kurosu T, Uratsuka S, Maeno H, Kozu T (1999) Texture statistics for classification of land use with multitemporal JERS-1 SAR single-look imagery. IEEE Trans Geosci Remote Sens 37(1):227–235. https://doi.org/10.1109/36.739157
Kuzucu AK, Balcik FB (2017) Testing the potential of vegetation indices for land use/cover classification using high resolution data. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 4:279
Lillesand TM, Kiefer RW, Chipman JW (2004) Remote sensing and image interpretation, 5th edn. Wiley, New York
Lu D, Weng Q (2006) Use of impervious surface in urban land-use classification. Remote Sens Environ 102:146–160. https://doi.org/10.1016/j.rse.2006.02.010
Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 28(5):823–870. https://doi.org/10.1080/01431160600746456
Magesh NS, Chandrasekar N, Soundranayagam JP (2012) Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing. GIS and MIF techniques. Geosci Front 3(2):189–196. https://doi.org/10.1016/j.gsf.2011.10.007
Mishra P, Singh D, Yamaguchi Y (2011) Land cover classification of palsar images by knowledge based decision tree classifier and supervised classifiers based on SAR observables. Prog Electromagn Res B 30:47–70. https://doi.org/10.2528/PIERB11011405
Mishra VN, Kumar P, Gupta DK, Prasad R (2014) Classification of various land features using Risat-1 dual polarimetric data. Int Arch Photogramm Remote Sens Spat Inf Sci (ISPRS Arch XL) 8:833–837. https://doi.org/10.5194/isprsarchives-xl-8-833-2014
Mishra VN, Prasad R, Kumar P et al (2017) Dual-polarimetric C-band SAR data for land use/land cover classification by incorporating textural information. Environ Earth Sci. https://doi.org/10.1007/s12665-016-6341-7
Muthukumarasamy I, Shanmugam RS, Kolanuvada SR (2017) SAR polarimetric decomposition with ALOS PALSAR-1 for agricultural land and other land use/cover classification: case study in Rajasthan, India. Environ Earth Sci 76:455. https://doi.org/10.1007/s12665-017-6783-6
Ndi Nyoungui A, Tonye E, Akono A (2002) Evaluation of speckle filtering and texture analysis methods for land cover classification from SAR images. Int J Remote Sens 23(9):1895–1925. https://doi.org/10.1080/01431160110036157
Ng WT, Rima P, Einzmann K et al (2017) Assessing the potential of Sentinel-2 and pléiades data for the detection of prosopis and vachellia spp. in Kenya. Remote Sens. https://doi.org/10.3390/rs9010074
Niu X, Ban Y (2013) Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach. Int J Remote Sens 34(1):1–26. https://doi.org/10.1080/01431161.2012.700133
NRSC (2011) RESOURCESAT-2 Manual. http://www.euromap.de/download/R2_data_user_handbook.pdf
de Oliveira Silveira EM, de Menezes MD, Acerbi Júnior FW, Santos Terra MCN, de Mello JM (2017) Assessment of geostatistical features for object-based image classification of contrasted landscape vegetation cover. J Appl Remote Sens 11(3):036004. https://doi.org/10.1117/1.JRS.11.036004
Omar H, Misman MA, Kassim AR (2017) Synergetic of PALSAR-2 and Sentinel-1A SAR polarimetry for retrieving aboveground biomass in dipterocarp forest of Malaysia. Appl Sci. https://doi.org/10.3390/app7070675
Parihar N, Rathore VS, Mohan S (2016) Combining ALOS PALSAR and AVNIR-2 data for effective land use/land cover classification in Jharia coalfields region. Int J Image Data Fusion. https://doi.org/10.1080/19479832.2016.1273258
Sambodo KA, Indriasari N (2013) Land cover classification of Alos Palsar data using support vector machine. Int J Remote Sens Earth Sci 10(1):9–18. https://doi.org/10.1128/JB.00758-15
Solberg AHS, Jain AK, Taxt T (1994) Multisource classification of remotely sensed data: fusion of Landsat TM and SAR images. IEEE Trans Geosci Remote Sens 32(4):768–778. https://doi.org/10.1109/36.298006
Stramondo S, Bignami C, Chini M et al (2006) Satellite radar and optical remote sensing for earthquake damage detection: results from different case studies. Int J Remote Sens 27(20):4433–4447. https://doi.org/10.1080/01431160600675895
Thanabalan P, Vidhya R (2016) A synergistic approach for soil moisture estimation using modified Dubois model with dual polarized SAR and optical satellite data. Land Surf Cryosphere Remote Sens III 9877:98770Y. https://doi.org/10.1117/12.2223751
Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Nueral Netw 10(5):988–999
Verhegghen A, Eva H, Ceccherini G et al (2016) The potential of sentinel satellites for burnt area mapping and monitoring in the Congo Basin forests. Remote Sens 8(986):1–22. https://doi.org/10.3390/rs8120986
Walsh SJ, McCleary AL, Mena CF, Shao Y, Tuttle JP, González A, Atkinson R (2008) QuickBird and Hyperion data analysis of an invasive plant species in the Galapagos Islands of Ecuador: implications for control and land use management. Remote Sens Environ 112(5):1927–1941
Ward D, Phinn SR, Murray AT (2000) Monitoring growth in rapidly urbanizing areas using remotely sensed data. Prof Geogr 52(3):371–386
Wei L, Hu Z, Gu M et al. (2012) Texture feature analysis in oil spill monitoring by SAR image. In: IEEE - 2012 20th international conference on geoinformatics, pp 1–6. https://doi.org/10.1109/Geoinformatics.2012.6270284
Wu S, Xu B, Wang L (2006) Urban land-use classification using variogram-based analysis with an aerial photograph. Photogramm Eng Remote Sens 72(7):813–822. https://doi.org/10.14358/PERS.72.7.813
Wu X, Peng J, Shan J, Cui W (2015) Evaluation of semivariogram features for object-based image classification. Geospat Inf Sci 18(4):159–170. https://doi.org/10.1080/10095020.2015.1116206
Zakeri H, Yamazaki F, Liu W (2017) Texture analysis and land cover classification of Tehran using polarimetric synthetic aperture radar imagery. Appl Sci. https://doi.org/10.3390/app7050452
Zhang H, Lin H, Li Y (2015) Impacts of feature normalization on optical and SAR data fusion for land use/land cover classification. IEEE Geosci Remote Sens Lett 12(5):1061–1065. https://doi.org/10.1109/LGRS.2014.2377722
Zou T, Yang W, Dai D, Sun H (2010) Polarimetric SAR image classification using multifeatures combination and extremely randomized clustering forests. EURASIP J Adv Signal Process. https://doi.org/10.1155/2010/465612
Acknowledgements
We are grateful to NRSC-ISRO, NASA and ESA for providing optical and SAR data sets for this study. I sincerely thank Dr. Kari Ramu and Dr. Mehmuna Bagum, Scientist, National Centre for Coastal Research (NCCR), Ministry of Earth Sciences, Government of India for their valuable suggestions.
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Muthukumarasamy, I., S. Shanmugam, R. & Usha, T. Incorporation of textural information with SAR and optical imagery for improved land cover mapping. Environ Earth Sci 78, 643 (2019). https://doi.org/10.1007/s12665-019-8654-9
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DOI: https://doi.org/10.1007/s12665-019-8654-9