Remote sensing detection enhancement

Ma, Tian J.

doi:10.1186/s40537-021-00517-8

Title: Remote sensing detection enhancement

Journal Article · Sat Oct 02 00:00:00 EDT 2021 · Journal of Big Data

DOI:https://doi.org/10.1186/s40537-021-00517-8· OSTI ID:1834123

Ma, Tian J. ^[1]

Sandia National Lab. (SNL-CA), Livermore, CA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Big Data in the area of Remote Sensing has been growing rapidly. Remote sensors are used in surveillance, security, traffic, environmental monitoring, and autonomous sensing. Real-time detection of small moving targets using a remote sensor is an ongoing, challenging problem. Since the object is located far away from the sensor, the object often appears too small. The object’s signal-to-noise-ratio (SNR) is often very low. Occurrences such as camera motion, moving backgrounds (e.g., rustling leaves), low contrast and resolution of foreground objects makes it difficult to segment out the targeted moving objects of interest. Due to the limited appearance of the target, it is tough to obtain the target’s characteristics such as its shape and texture. Without these characteristics, filtering out false detections can be a difficult task. Detecting these targets, would often require the detector to operate under a low detection threshold. However, lowering the detection threshold could lead to an increase of false alarms. In this paper, the author will introduce a new method that improves the probability to detect low SNR objects, while decreasing the number of false alarms as compared to using the traditional baseline detection technique.

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Research Organization:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

OSTI ID:: 1834123

Report Number(s):: SAND-2021-12291J; 700427

Journal Information:: Journal of Big Data, Vol. 8, Issue 1; ISSN 2196-1115

Publisher:: BioMed CentralCopyright Statement

Country of Publication:: United States

Language:: English

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