Articles | Volume 10, issue 1
https://doi.org/10.5194/jsss-10-127-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.A classification technique of civil objects by artificial neural networks using estimation of entropy on synthetic aperture radar images
Related subject area
Applications: Environmental monitoring
An in-hive soft sensor based on phase space features for Varroa infestation level estimation and treatment need detection
Measure particulate matter by yourself: data-quality monitoring in a citizen science project
An autonomous flame ionization detector for emission monitoring
Metal ion binding and tolerance of bacteria cells in view of sensor applications
J. Sens. Sens. Syst., 11, 29–40,
2022J. Sens. Sens. Syst., 8, 317–328,
2019J. Sens. Sens. Syst., 8, 67–73,
2019J. Sens. Sens. Syst., 7, 433–441,
2018Cited articles
Cho, J. H. and Park, C. G.: Multiple Feature Aggregation Using Convolutional
Neural Networks for SAR Image-Based Automatic Target Recognition, IEEE Geosci. Remote Sens. Lett., 15, 1882–1886, https://doi.org/10.1109/LGRS.2018.2865608, 2018.
Doo, S. H., Smith, G. E., and Baker, C. J.: Aspect invariant features for radar target recognition, IET Radar Sonar Navigat., 11, 597–604, https://doi.org/10.1049/iet-rsn.2016.0075, 2017.
El-Darymli, K., Gill, E. W., Mcguire, P., Power, D., and Moloney, C.: Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review, IEEE Access, 4, 6014–6058, https://doi.org/10.1109/ACCESS.2016.2611492, 2016.
Ernisse, B. E., Rogers, S. K., DeSimio, M., and Raines, R. A.: An automatic target cuer/recognizer for tactical fighters, in: vol. 3, 1997 IEEE Aerospace Conference, Snowmass, Aspen, CO, USA, 441–455, https://doi.org/10.1109/AERO.1997.574899, 1997.
Gini, F.: Knowledge Based Radar Detection, Tracking and Classification,
Wiley-Interscience publication, Canada, p. 273, 2008.