Industrial internet of things has grown quite popular in recent years and involves a large number of intelligent devices linked together to build a system that can investigate, communicate, gather and observe information. Due to this requirement, there is more demand for compression techniques which compresses data, leading to less usage of resources and low complexity. This is where Convolutional Neural Networks (CNN) play a large role in the field of computer vision, especially in places where high applications such as interpretation coupled with detection is required. Similarly, low-level applications such as image compression cannot be resolved using this methodology. In this paper, a compression technique for remote sensing images using CNN is proposed. This methodology incorporates CNN in a compact learning environment wherein the actual image that consists of structural data is coded using Lempel Ziv Markov chain algorithm. This process is followed by image reconstruction in order to obtain the actual image in high quality. Other methodologies such as optimized trunctiona, JPEG2000, JPEC and binary tree were compared using a large number of experiments in terms of space saving, reconstructed image quality and efficiency. The output obtained indicates that the proposed methodology shows effective improvement, attaining a 50 dB signal to noise ratio and space saving of 90%.
Predicting the Lossless Compression Ratio of Remote Sensing Images with Configurational Entropy
