Remote Sensing Low Signal-to-Noise-Ratio Target Detection Enhancement is research paper by Tian J. Ma and Robert J. Anderson from Sandia National Laboratories.

In the realm of real-time remote sensing applications, data frames continuously stream into the processing system. The ability to identify and monitor objects of interest as they navigate through space holds paramount importance for numerous critical surveillance and monitoring missions. Effectively detecting small objects via remote sensors remains an ongoing and intricate challenge. Given that these objects are situated at considerable distances from the sensor, their Signal-to-Noise Ratio (SNR) tends to be low. The Limit of Detection (LOD) for remote sensors is essentially confined to what can be observed within each individual image frame.

This paper introduces a novel approach, the “Multi-frame Moving Object Detection System (MMODS)”, designed to identify small objects with low SNR that might surpass human observation within a single video frame. This concept is substantiated through simulated data, wherein our technology can detect objects as minute as a single pixel, possessing a targeted SNR approaching 1:1. A similar enhancement is showcased through real-time data acquired from a remote camera. MMODS technology effectively bridges a significant gap in remote sensing surveillance applications pertaining to the detection of diminutive targets. Our methodology does not necessitate prior knowledge about the environment, pre-identified targets, or training data to proficiently discern and track both slow-moving and fast-moving targets, irrespective of their size or distance.

Publication Date: March 2023

Remote Sensing Low Signal-to-Noise-Ratio Target Detection Enhancement contains the following main sections:

  • Introduction
  • Materials and Methods
  • Results and Discussions
  • Conclusions
  • Patents

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Authors- Tian J. Ma and Robert J. Anderson

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See also- Sandia Research Enhances Object Detection

Post Image- MMODS Detection on Sandia Mountain Peak (Image Credit: Ma and Anderson)