skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Scalable Track Detection in SAR CCD Images

Abstract

Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images ta ken at different times of the same scene, rely on simple, fast models to label track pixels. These models, however, are often too simple to capture natural track features such as continuity and parallelism. We present a simple convolutional network architecture consisting of a series of 3-by-3 convolutions to detect tracks. The network is trained end-to-end to learn natural track features entirely from data. The network is computationally efficient and improves the F-score on a standard dataset to 0.988, up fr om 0.907 obtained by the current state-of-the-art method.

Authors:
 [1];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation (NA-20)
OSTI Identifier:
1347496
Report Number(s):
SAND2017-2423
651687
DOE Contract Number:
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION

Citation Formats

Chow, James G, and Quach, Tu-Thach. Scalable Track Detection in SAR CCD Images. United States: N. p., 2017. Web. doi:10.2172/1347496.
Chow, James G, & Quach, Tu-Thach. Scalable Track Detection in SAR CCD Images. United States. doi:10.2172/1347496.
Chow, James G, and Quach, Tu-Thach. 2017. "Scalable Track Detection in SAR CCD Images". United States. doi:10.2172/1347496. https://www.osti.gov/servlets/purl/1347496.
@article{osti_1347496,
title = {Scalable Track Detection in SAR CCD Images},
author = {Chow, James G and Quach, Tu-Thach},
abstractNote = {Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images ta ken at different times of the same scene, rely on simple, fast models to label track pixels. These models, however, are often too simple to capture natural track features such as continuity and parallelism. We present a simple convolutional network architecture consisting of a series of 3-by-3 convolutions to detect tracks. The network is trained end-to-end to learn natural track features entirely from data. The network is computationally efficient and improves the F-score on a standard dataset to 0.988, up fr om 0.907 obtained by the current state-of-the-art method.},
doi = {10.2172/1347496},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2017,
month = 3
}

Technical Report:

Save / Share:
  • This project developed a new scalable network firewall and Intrusion Protection System (IPS) that can manage increasing traffic loads, higher network speeds, and strict Quality of Service (QoS) requirements. This new approach provides a strong foundation for next-generation network security technologies and products that address growing and unmet needs in the government and corporate sectors by delivering Optimal Network Security. Controlling access is an essential task for securing networks that are vital to private industry, government agencies, and the military. This access can be granted or denied based on the packet header or payload contents. For example, a simple networkmore » firewall enforces a security policy by inspecting and filtering the packet headers. As a complement to the firewall, an Intrusion Detection System (IDS) inspects the packet payload for known threat signatures; for example, virus or worm. Similar to a firewall policy, IDS policies consist of multiple rules that specify an action for matching packets. Each rule can specify different items, such as the signature contents and the signature location within the payload. When the firewall and IDS are merged into one device, the resulting system is referred to as an Intrusion Protection System (IPS), which provides both packet header and payload inspections. Having both types of inspections is very desirable and more manageable in a single device.« less