Feature Enhancement of Multispectral Images Using Vegetation, Water, and Soil Indices Image Fusion
Land cover characteristics of satellite images are analyzed in this research paper. Remote sensing indices are calculated for multispectral image. In the proposed method, satellite image indices, i.e., NDVI (Normalized difference vegetation index), NDWI (Normalized difference water index), and BSI (Bare soil index), are calculated for various classes such as land, vegetation, water, and in land cover categories. All these remote sensing indices are fused to get composite bands and to enhance all features in multispectral image. This technique increases visual perception of human eye for multispectral images. Fusion plays vital role in remote sensing and medical images interpretation. In case of remote sensing, we cannot get entire information in one spectral band. So multispectral bands are combined, which leads to feature enhancement. This method depends on green (G), infrared (IR), near infrared (NIR), and short wave infrared (SWIR) bands and their fusion. Finally, error matrix is generated with reference data and classified data. The main application is to calculate vegetation, bare soil, and water indices in three land covers and to get better feature enhancement. Producer’s accuracy, consumer’s accuracy, commission, omission, kappa coefficient, F1score, over all accuracy, and over all kappa coefficients are calculated.
KeywordsLand cover NDVI NDWI BSI Error matrix
Authors are grateful to Bhuvan for providing satellite images to our research work.
- 1.Gonzalez RC, Woods RE (2008) Digital image processing. Prentice Hall, New JerseyGoogle Scholar
- 3.Lillesand TM, Kiffer RW (2000) Remote sensing and image interpretation, 4th edn. Wiley, New YorkGoogle Scholar
- 8.Hudson WD, Ramm CW (1987) Correct formulation of the kappa coefficient of agreement. Photogram Eng Remote Sens 53:421–422Google Scholar
- 10.Molchanov V, Chitiboi T, Linsen L (2015) Visual analysis of medical image segmentation feature space for interactive classification. In: Eurographics conference, pp 11–19Google Scholar