Improved normalised difference vegetation index method based on discrete cosine transform and singular value decomposition for satellite image processing

Abstract

In this study, an improved multi-band satellite contrast enhancement technique based on the singular value decomposition (SVD) and discrete cosine transform (DCT) has been proposed for the feature extraction of low-contrast satellite images using normalised difference vegetation index (NDVI) technique. The method employs multi-spectral remote sensing data technique to find the spectral signature of different objects such as the vegetation index and land cover classification presented in the satellite image. The proposed technique converts the image into the SVD–DCT domain and after normalising the singular value matrix; the enhanced image is reconstructed by using inverse DCT. The visual and quantitative results included in this study clearly show the increased efficiency and flexibility of the proposed method over the existing methods. The simulation results show that the enhancement-based NDVI using DCT–SVD technique is highly useful to detect the surface features of the visible area which are extremely beneficial for municipal planning and management.