Hyperspectral remote sensing data compression and protection

Abstract

In this paper, we consider methods for hyperspectral image processing, required in systems of image formation, storage, and transmission and aimed at solving problems of data compression and protection. A modification of the digital image compression method based on a hierarchical grid interpolation is proposed. Methods of active (on the basis of digital watermarking) and passive (on the basis of artificial image distortion detection) data protection against unauthorized dissemination are developed and investigated.

Current problems of development of the journal of Computer Optics

“…Changes have also taken place in the journal contentnew publication categories have appeared: Earth's Remote Sensing Technology, including development of hyperspectral equipment [6][7][8][9]; photonic-crystal sensors [10][11]; LED technology [12][13]; video signal stream processing and some other new methods of image subject processing [14][15][16][17][18]; new types of laser beams [19][20]; and academic reviews of up-to-date trends have been published [21][22]. In 2016, for the first time in a long period, a full-text English version of the journal of Computer Optics was issued with the articles originally written in English (Volume 40, Issue 5), which had not previously been published elsewhere.…”

Spatio-Temporal Slices for Frame Cut Detection in Video

“…In practice, the detection of artificial changes in video can be carried out using digital forensics methods developed since the second half of the 2000s [4][5][6]. The main achievements in this direction are associated with H. Farid, A.C. Popescu, S. Prasad, J. Fridrich, A. Piva, M. Barni.…”

Hyperspectral regression lossless compression algorithm of aerospace images

“…, 0 (2020) https://doi.org/10.1051/e3sconf /20201490 E3S Web of Conferences 149 0 RPERS 2019 20 200 3 3 Based on researches of hyperspectral AI in the field of compression [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] presented in the works of scientists from Russia, China, the USA, India, etc., it can be assumed that the developed methods and lossless compression algorithms for hyperspectral AI can be improved by reducing their computational efficiency and increasing the compression ratio by modifying the preprocessing steps using mathematical methods. In addition, new compression preprocessing steps can be proposed that effectively increase the compression ratio and reduce the compression process time.…”