Abstract
In the present paper, classification and analysis of complex land features on the combined outcome of the high spectral resolution Hyperion-Hyperspectral Remote Sensing (HRS) data and high spatial resolution Resourcesat-II Linear Imaging Self-Scanning System IV (LISS-IV) multispectral data were investigated. The traditional way of satellite image fusion is based on high spatial resolution panchromatic image and low spatial-spectral resolution multispectral image. However, in the current study, a novel approach via considering HRS and LISS-IV multispectral data is proposed for classification of complex features of earth surface. The used multi-date, multi-sensor and multi-resolution satellite imagery was acquired on \(20^{th}\) March, 2015 and \(28^{th}\) February, 2014 of HRS and LISS-IV data having spatial resolution 30 m and 5.8 m respectively. Three pixel level image fusion algorithms were computed such as Gram-Schmidt Transform (GST), Principal Component Spectral Sharpening Transform (PCSST) and Color Normalized Spectral Sharpening (CNSS) for fusion of datasets. The quality of the fusion algorithms has been estimated on the classification accuracy of mixed features. Moreover, the performance of three fusion algorithms was compared with the classification results. The assessment results of fused data using all the methods were acceptable in view of spatial-spectral accuracy of data. The Support Vector Machine (SVM) approach with its Gaussian Radial Basis Function (GRBF) kernel was implemented for classification of original and fused data. In conclusion, the SVM algorithm resulted accurate with 97.65, 97.47, 96.30, 86.20 and 74.44% accuracy for CNSS fused, PCSST fused, GST fused, and original multispectral and original hyperspectral data respectively and proved very robust method for mixed feature classification.
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Zoleikani, R., Zoej, M.V., Mokhtarzadeh, M.: Comparison of pixel and object oriented based classification of hyperspectral pansharpened images. J. Indian Soc. Remote Sens. 45(1), 25–33 (2017)
Debes, C., et al.: Hyperspectral and LiDAR data fusion: outcome of the 2013 GRSS data fusion contest. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(6), 2405–2418 (2014)
Rajput, U.K., Ghosh, S.K., Kumar, A.: Comparison of fusion techniques for very high resolution data for extraction of urban land-cover. J. Indian Soc. Remote Sens. 45(4), 709–724 (2017)
Benediktsson, J.A., Palmason, J.A., Sveinsson, J.R.: Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Trans. Geosci. Remote Sens. 43(3), 480–491 (2005)
Benediktsson, J.A., Pesaresi, M., Amason, K.: Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Trans. Geosci. Remote Sens. 41(9), 1940–1949 (2003)
Dalponte, M., Bruzzone, L., Gianelle, D.: Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas. IEEE Trans. Geosci. Remote Sens. 46(5), 1416–1427 (2008)
Fauvel, M., Benediktsson, J.A., Chanussot, J., Sveinsson, J.R.: Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Trans. Geosci. Remote Sens. 46(11), 3804–3814 (2008)
Benediktsson, J. A., Palmason, J. A., Sveinsson, J. R., Chanussot, J.: Decision level fusion in classification of hyperspectral data from urban areas. In: 2004 Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2004, vol. 1. IEEE, September 2004
Swatantran, A., Dubayah, R., Roberts, D., Hofton, M., Blair, J.B.: Mapping biomass and stress in the Sierra Nevada using LiDAR and hyperspectral data fusion. Remote Sens. Environ. 115(11), 2917–2930 (2011)
Abbasi, B., Arefi, H., Bigdeli, B., Motagh, M., Roessner, S.: Fusion of hyperspectral and LiDAR data based on dimension reduction and maximum likelihood. Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. 40(7), 569 (2015)
Man, Q., Dong, P., Guo, H.: Pixel-and feature-level fusion of hyperspectral and LiDAR data for urban land-use classification. Int. J. Remote Sens. 36(6), 1618–1644 (2015)
Kumar, U., Milesi, C., Nemani, R.R., Basu, S.: Multi-sensor multi-resolution image fusion for improved vegetation and urban area classification. Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. 40(7), 51 (2015)
Vibhute, A.D., Kale, K.V., Dhumal, R.K., Mehrotra, S.C.: Hyperspectral imaging data atmospheric correction challenges and solutions using QUAC and FLAASH algorithms. In: 2015 Proceedings of the International Conference on Man and Machine Interfacing (MAMI), pp. 1–6. IEEE, December 2015
Tsagaris, V., Anastassopoulos, V.: Multispectral image fusion for improved RGB representation based on perceptual attributes. Int. J. Remote Sens. 26(15), 3241–3254 (2005)
Ashraf, S., Brabyn, L., Hicks, B.J.: Image data fusion for the remote sensing of freshwater environments. Appl. Geogr. 32(2), 619–628 (2012)
Basaeed, E., Bhaskar, H., Al-Mualla, M.: Comparative analysis of pan-sharpening techniques on DubaiSat-1 images. In: 2013 Proceedings of the 16th International Conference on Information Fusion (FUSION), pp. 227–234. IEEE, July 2013
https://www.harrisgeospatial.com/docs/gramschmidtspectralsharpening.html
https://www.harrisgeospatial.com/docs/pcspectralsharpening.html
https://www.harrisgeospatial.com/docs/cnspectralsharpening.html
Ehlers, M., Klonus, S., Johan Åstrand, P., Rosso, P.: Multi-sensor image fusion for pansharpening in remote sensing. Int. J. Image Data Fusion 1(1), 25–45 (2010)
Hsu, S.M., Burke, H.H.: Multisensor fusion with hyperspectral imaging data: detection and classification. In: Handbook of Pattern Recognition and Computer Vision, pp. 347–364 (2005)
Vibhute, A.D., Kale, K.V., Mehrotra, S.C., Dhumal, R.K., Nagne, A.D.: Determination of soil physicochemical attributes in farming sites through visible, near-infrared diffuse reflectance spectroscopy and PLSR modeling. Ecol. Process. 7(1), 26 (2018)
Vibhute, A.D., Dhumal, R.K., Nagne, A.D., Rajendra, Y.D., Kale, K.V., Mehrotra, S.C.: Analysis, classification, and estimation of pattern for land of Aurangabad region using high-resolution satellite image. In: Satapathy, S.C., Raju, K.S., Mandal, J.K., Bhateja, V. (eds.) Proceedings of the Second International Conference on Computer and Communication Technologies. AISC, vol. 380, pp. 413–427. Springer, New Delhi (2016). https://doi.org/10.1007/978-81-322-2523-2_40
Beck, R.: EO-1 user guide, version 2.3. Satellite Systems Branch, USGS Earth Resources Observation Systems Data Center (EDC) (2003)
Vapnik, V.N.: An overview of statistical learning theory. IEEE Trans. Neural Netw. 10(5), 988–999 (1999)
Heras, D.B., Argüello, F., Quesada-Barriuso, P.: Exploring ELM-based spatial-spectral classification of hyperspectral images. Int. J. Remote Sens. 35(2), 401–423 (2014)
Richards, J.A., Jia, X.: Remote Sensing Digital Image Analysis, vol. 3. Springer, Heidelberg (2006)
Zhang, C., Wang, T., Atkinson, P.M., Pan, X., Li, H.: A novel multi-parameter support vector machine for image classification. Int. J. Remote Sens. 36(7), 1890–1906 (2015)
Widjaja, E., Zheng, W., Huang, Z.: Classification of colonic tissues using near-infrared Raman spectroscopy and support vector machines. Int. J. Oncology 32(3), 653–662 (2008)
Gao, J.: Digital Analysis of Remotely Sensed Imagery. McGraw-Hill Professional (2008)
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The authors would like to thanks to the United States Geological Survey (USGS) for providing EO-1 Hyperion Data for this study.
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Vibhute, A.D. et al. (2019). Hyperspectral and Multispectral Remote Sensing Data Fusion for Classification of Complex-Mixed Land Features Using SVM. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1035. Springer, Singapore. https://doi.org/10.1007/978-981-13-9181-1_31
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DOI: https://doi.org/10.1007/978-981-13-9181-1_31
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