Separability Analysis of The Band Combinations For Land Cover Classification of Satellite Images

Separability Analysis of The Band Combinations For Land Cover Classification of Satellite Images

© 2021 by IJETT Journal
Volume-69 Issue-8
Year of Publication : 2021
Authors : Keerti Kulkarni, Dr. P. A. Vijaya
DOI :  10.14445/22315381/IJETT-V69I8P217

How to Cite?

Keerti Kulkarni, Dr. P. A. Vijaya, "Separability Analysis of The Band Combinations For Land Cover Classification of Satellite Images," International Journal of Engineering Trends and Technology, vol. 69, no. 8, pp. 138-144, 2021. Crossref,

Land cover classification is one of the important applications of satellite images. The accuracy of the classification process depends on the feature selection. In multispectral satellite images, the separability of the features depends on the band combinations used. This work demonstrates the change in the accuracy of the classifiers with different band combinations and different distance measures used for analyzing the separability. Landsat-8 images have been classified into four land cover classes using the Maximum Likelihood (ML) Classifier, Minimum Distance (MD) Classifier, and the Spectral Angle (SA) mapper. The spectral separability between each of the land cover classes is analyzed for the band combinations of 3-2, 4-3-2, 3-2-1, 7-6-5-4, 7-6-5-4-3, using the Jeffries-Matusita Distance measure, the Euclidean Distance, and the spectral angle measure. It is shown that maximum separability and hence the optimal accuracy of 85.81% is obtained with a SA mapper using the spectral angle measure on a three-band combination of 4-3-2. An accuracy of 80.12% is achieved with an ML classifier using Jeffries-Matusita distance measure with a band combination of 4-3-2. Lastly, the MD classifier gives an accuracy of 76.56% using the Euclidean distance measure with a band combination of 4-3-2.

Band combination, Euclidean Distance, Jeffries-Matusita Distance, Maximum Likelihood Classifier, Minimum Distance Classifier, Spectral angle, Spectral Angle Mapper.

[1] C. Deng and C. Wu., BCI: A biophysical composition index for remote sensing of urban environments, Remote Sens. Environ., 127 (2020) 247–259.
[2] Congalton, R.G., and K. Green., Assessing the accuracy of remotely sensed data: principles and practices, CRC press, (2008).
[3] Coggeshall, M.E. and R.M. Hoffer., Basic forest cover mapping using digitized remote sensor data and automated data processing techniques, LARS Technical Reports. Paper 23. (1973).
[4] Davis, S.M., et al., Remote sensing: the quantitative approach”. New York, McGraw-Hill International Book Co, L (1978).
[5] Duda, R.O., P.E. Hart, and D.G. Stork ., Pattern classification, Wiley New York, 2 (1973).
[6] Elhag, M., A. Psilovikos, and M. Sakellariou., Detection of land cover changes for water recourses management using remote sensing data over the Nile Delta Region, Environment, Development and Sustainability, 15(5) (2013)1189-1204.
[7] Friedl, M.A., et al., MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote sensing of Environment, 114(1) (2010) 168-182.
[8] Gislason, P.O., J.A. Benediktsson, and J.R. Sveinsson., “Random forests for land cover classification. Pattern Recognition Letters, 27(4) (2006) 294-300.
[9] Gong, P., et al., Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data. International Journal of Remote Sensing, 34(7) (2013) 2607-2654.
[10] H. Huang et al., Separability Analysis of Sentinel-2A Multi-Spectral Instrument (MSI) Data for Burned Area Discrimination, Remote Sensing 8(10) (2016) 873
[11] Li, C., et al., Comparison of classification algorithms and training sample sizes in urban land classification with Landsat thematic mapper imagery. Remote Sensing, 6(2) (2014) 964-983
[12] Lunetta, R.S. and M.E. Balogh., Application of multi-temporal Landsat 5 TM imagery for wetland identification. Photogrammetric Engineering and Remote Sensing. 65(11) (1999) 1303-1310.
[13] Mallinis, G. et al., Forest parameters estimation in a European Mediterranean landscape using remotely sensed data. Forest Science, 50(4) (2004) 450-460.
[14] Kulkarni K, Dr. P. A. Vijaya., Parametric Approaches to Multispectral Image Classification using Normalized Difference Vegetation Index, International Journal of Innovative Technology and Exploring Engineering, 9(2S) (2019) DOI: 10.35940/ijitee.B1061.1292S19, pp 611-615.
[15] Kulkarni K, Dr. P. A. Vijaya ., Experiment of Multispectral Images using Spectral Angle Mapper Algorithm for Land Cover Classification, International Journal of Innovative Technology and Exploring Engineering, 8(6S4),(2019) 96-99.
[16] Richards, J.A. and J. Richards ., Remote sensing digital image analysis, 3 (1999), Springer.
[17] Rogan, J., et al., Land-cover change monitoring with classification trees using Landsat TM and ancillary data, Photogrammetric Engineering & Remote Sensing, 69(7) (2003) 793-804.
[18] S. Amelinckx ., Spatial and spectral separability of grasslands in the inner Turku Archipelago using landsat thematic mapper satellite imagery., Masters Thesis, University of Turku, Finland, (2010).
[19] Dhaka Suman, Shankar Hari, P.S.Roy, Kiran Raj ., IRS P6 LISS-IV Image Classification using Simple, Fuzzy Logic and Artificial Neural Network Techniques: A Comparison Study, International Journal of Technical Research & Science, 1(2) (2016).
[20] Ye, Chul-Soo, Evaluating the Contribution of Spectral Features to Image Classification Using Class Separability, Korean Journal of Remote Sensing, 36(1) (2020) 55-65,
[21] Bellman, R ., Dynamic programming, Princeton University Press, (1953).
[22] for-landsat-8/
[23] Mishra P.K, A. Rai, and S. C. Rai, Land use and land cover change detection using geospatial techniques in the Sikkim Himalaya, India, Egypt. J. Remote Sens. Sp. Sci., 23(2) (2020) 133–143.
[24] E. Venkateswarlu, T. Sivannarayana, and K. V. R. Kumar, A comparative analysis of Resources at-2 LISS-3 and LANDSAT-8 OLI imagery, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., 40(8) (2014) 987–989.
[25] Gandhi G. M., S. Parthiban, N. Thummalu, and A. Christy, Ndvi: Vegetation Change Detection Using Remote Sensing And Gis - A Case Study of Vellore District, Procedia Comput. Sci., 57 (2015) 1199–1210.