Remote sensing detection enhancement

• Published in: Journal of big data Vol. 8; no. 1; pp. 1 - 13
• Main Author: Ma, Tian J.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 02-10-2021; Springer Nature B.V; BioMed Central; SpringerOpen
• Subjects: Alarms; Big Data; Communications Engineering; Computational Science and Engineering; Computer Science; Constrained velocity matched filter; Data Mining and Knowledge Discovery; Database Management; Environmental monitoring; False alarms; Information Storage and Retrieval; Low SNR object detection; Mathematical Applications in Computer Science; Methodology; Moving object recognition; Moving targets; Networks; OTHER INSTRUMENTATION; Remote sensing; Remote sensors; Signal to noise ratio; Small object detection; Target detection; Track-Before-Detect; Traffic surveillance; Velocity matched filter; Small object detection; Constrained velocity matched filter; Track-Before-Detect; Remote sensing; Low SNR object detection; Velocity matched filter
• ISSN: 2196-1115; 2196-1115
• DOI: 10.1186/s40537-021-00517-8

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.