Development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations

Assiya Sarinova,G.M Tsybulski Gennady,Yu.A Maglinets,M.V Noskov

doi:10.1051/e3sconf/202133301011

Abstract

The paper describes the development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations for the purpose of subsequent compression in Earth remote sensing systems. As compression algorithms necessary to reduce the amount of transmitted information, it is proposed to use the developed compression methods based on Walsh-Hadamard transformations and discrete-cosine transformation. The paper considers a methodology for developing lossy and high-quality compression algorithms during recovery, taking into account which an adaptive algorithm for compressing hyperspectral AI and the generated quantization table has been developed. The conducted studies have shown that the proposed lossy algorithms have sufficient efficiency for use and can be applied when transmitting hyperspectral remote sensing data in conditions of limited buffer memory capacity and bandwidth of the communication channel.

Highlights

Hyperspectral AI remote sensing is necessary for monitoring natural resources and the consequences of emergencies, etc
There are various areas of research in which research is actively being conducted in the field of developing compression algorithms [1-13]
Algorithms for processing hyperspectral AI with losses based on discrete transformations have been developed

Summary

Assiya Sarinova

The paper describes the development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations for the purpose of subsequent compression in Earth remote sensing systems. As compression algorithms necessary to reduce the amount of transmitted information, it is proposed to use the developed compression methods based on Walsh-Hadamard transformations and discretecosine transformation. The paper considers a methodology for developing lossy and highquality compression algorithms during recovery, taking into account which an adaptive algorithm for compressing hyperspectral AI and the generated quantization table has been developed. The conducted studies have shown that the proposed lossy algorithms have sufficient efficiency for use and can be applied when transmitting hyperspectral remote sensing data in conditions of limited buffer memory capacity and bandwidth of the communication channel

Introduction

Experiments of the developed algorithms of lossy transformations

Conclusion