Notice of Violation of IEEE Publication Principles: Reversible Data Hiding and Smart Multimedia Computing Using Big Data in Remote Sensing Systems

Notice of Violation of IEEE Publication Principles

“Reversible Data Hiding and Smart Multimedia Computing Using Big Data in Remote Sensing Systems”
by Rahul Malik, Aditya Khamparia, Sahil Garg, Deepak Gupta, Bong Jun Choi, M. Shamim Hossain in IEEE Access, Vol 8, August 2020, pp. 153546- 153560

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.

This paper is a duplication of the original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

“Big Data Geospatial Processing for Massive Aerial LiDAR Datasets”
by David Deibe, Margarita Amor, and Ramón Doallo in Remote Sensing, MDPI, February 2020


There is a need for improvement of tools to deal with large volumes of multimedia data effectively. In particular, real-time data processing is one of the major problems for multimedia data computing in remote sensing systems. Such big data systems have to offer effective management and computational efficiency for applications in real-time. In this paper, we propose a large-scale geological processing method for aerial Light Detection and Ranging (LiDAR) clouds containing multimedia data that ensures mobility and timeliness. By utilizing Spark and Cassandra, our proposed approach can significantly reduce the execution time of the time-consuming process. We investigate fast ground-only raster generation from huge LiDAR datasets. We observed that filtered cloud data ensuing from impartial consideration of neighboring zones could lead to classification errors on the boundaries. Therefore, an integrated approach is proposed to correct these errors to improve the classification consistency, achieve faster processing time, provide automatic error correction, obtain Digital Terrain Models (DTM), and minimize user intervention. These features can provide a framework for an on-demand DTM output and scalable application services. Furthermore, the proposed approach can expect to benefit other real-time applications in LiDAR systems.