Reduction of satellite images size in 5G networks using machine learning algorithms

Abstract

The high data volume of multispectral satellite images is compressed for better visual perception without loss of image and statistical properties of the local or global image to provide superior information for obtaining effective results using 5G networks. This compression is a technique applied to remote sensing applications to analyse the data for prediction or forecasting the real-time applications by remote sensing applications like IoT and data transmission over 5G wireless networks. The extensive data images have multiple bands, which contain earth surface/object information with various frequencies. It is difficult to handle this extensive data for processing data. The compression is mandatory to avoid this complexity by removing redundancy data, unnecessary pixel information and non-visual redundancy data between bands. There are various standard compression techniques are available like JPEG 2000, Wavelet and DCT methods. The proposed method is implemented with a combination of intra coding and machine learning algorithm. The standard compression technique does not give better results due to degradation of pixels, lack of spatial and spectral information. This paper enriches progressive results by reduced satellite images for transmission of data in IoT and 5G wireless networks, in which qualitative results are compared by standard compression technique with suitable parameters.