Data compression in ViSAR sensor networks using non-linear adaptive weighting

Abstract

Nowadays, industrial video synthetic aperture radars (ViSARs) are widely used for aerial remote sensing and surveillance systems in smart cities. A main challenge of a group of networked ViSAR sensors in an IoT-based environment is low bandwidth of wireless links for communicating big video data. In this research, we propose a non-linear statistical estimator for adaptive reconstruction of compressed ViSAR data. Our proposed reconstruction filter is based on an adaptively generated non-linear weight mask of spatial observations. It can strongly outperform several conventional and well-known reconstruction filters for three different video samples.

Mobile multimedia computing in cyber-physical surveillance services through UAV-borne Video-SAR: A taxonomy of intelligent data processing for IoMT-enabled radar sensor networks

“…Sending the video data instead of the raw data can be performed subject to hardware/physical facilities in the radar platforms to generate videos, and also management of the issue of overlapping to not have more data size in the video format compared to the raw data when both are uncompressed (some examples of the facilities are high-performance onboard processing devices, highcapacity memory, and required electrical power). In such a condition, we can select video data and benefit from all advantages of multimedia onboard computing and communications over a distributed mobile network [1,[11][12][13] . Onboard processing should be done in many remote sensing sensors towards data communications, for example, onboard processing of satellite Hyper-Spectral Imaging (HSI) data in spaceborne remote sensing is a crucial requirement.…”

“…As final point, there is a need to design stronger 2D processing techniques for the situation, such that more information around 2D and 3D Video-SAR data processing can be found in Refs. [1,[11][12][13][14][41][42][43]. 4.1.5 Machine intelligence: Detection, recognition, and tracking…”

“…In Refs. [1,[11][12][13], it was demonstrated that in addition to usual lossy/lossless video coding techniques to reduce the Video-SAR data size (in order for data communications to be easier in non-raw form), it is possible to use spatial domain processing, mainly as 2D, like spatial data resampling for single-source data compression and a kind of data embedding process for single-and multi-source data compression. Aggregation is a solution for doing multi-source data compression in multi-sensorial systems.…”

“…Recently, radar imaging systems have been applied for generating video sequences [1,2] . In order to make such an ability in radars, a new imaging mode was added to Synthetic Aperture Radars (SAR) which can produce a time sequence of images from the observed scene (or image region).…”

“…SAR videos can give us new horizons of technical reliability in real-world applications, such as real-time weather-independent green surveillance and target tracking systems based on active radars for nights where there is no light and therefore optical sensors are out of service. This new mode was named Video-SAR (in the literature, VideoSAR and ViSAR are also used [1] ). The starting point of research on Video-SAR has not been well specified and may be in 2003, however this topic is not important.…”