Detection and classification of small-scale objects in images obtained by synthetic-aperture radar stations
2018 ◽
pp. 93-99
Keyword(s):
The investigation deals with the problem of simultaneous detection and classification (that is, recognition) of several classes of objects in radar images by means of convolutional neural networks. We present a two-stage processing algorithm that detects and recognises objects. It also features an intermediate sub-stage that increases the resolution of those zones where objects have been detected. We show that a considerable increase in detection and recognition probabilities is possible if the recognition module is trained using high-resolution data. We implemented the detection and recognition stages using deep learning approaches for convolutional neural networks.
2017 ◽
Vol 63
(12)
◽
pp. 1847-1855
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2020 ◽
Vol 2020
(10)
◽
pp. 28-1-28-7
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2020 ◽
Vol 16
(9)
◽
pp. 1332
2019 ◽
Vol 7
(6)
◽
pp. 164-168
2019 ◽
Vol 277
◽
pp. 02024
◽