2019
DOI: 10.3390/rs11040424
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Self-Paced Convolutional Neural Network for PolSAR Images Classification

Abstract: Fully polarimetric synthetic aperture radar (PolSAR) can transmit and receive electromagnetic energy on four polarization channels (HH, HV, VH, VV). The data acquired from four channels have both similarities and complementarities. Utilizing the information between the four channels can considerably improve the performance of PolSAR image classification. Convolutional neural network can be used to extract the channel-spatial features of PolSAR images. Self-paced learning has been demonstrated to be instrumenta… Show more

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Cited by 9 publications
(4 citation statements)
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“…In order to classify all pixels in a patch of urban area simultaneously, a fixed-feature-size CNN was used in [17]. A deep self-paced CNN [18] was employed for river area classification, which retains mutual information between channels and texture information from the raw quad-pol SAR data. For building and vegetation classification [19], a quad-pol SAR image classification method based on residual network (ResNet) and deep autoencoder was proposed.…”
Section: A Related Workmentioning
confidence: 99%
“…In order to classify all pixels in a patch of urban area simultaneously, a fixed-feature-size CNN was used in [17]. A deep self-paced CNN [18] was employed for river area classification, which retains mutual information between channels and texture information from the raw quad-pol SAR data. For building and vegetation classification [19], a quad-pol SAR image classification method based on residual network (ResNet) and deep autoencoder was proposed.…”
Section: A Related Workmentioning
confidence: 99%
“…In the network model, the size of the dilated convolution kernel in each dilated convolution layer is 3 * 3, and the stride in the convolution operation is all set to 1. The network structure only uses a small-sized kernel because it can not only greatly reduce the parameters in the network structure, but also obtain better results [5]. The first dilated convolutional layer kernel size is 32 * 3 * 3 * d, where d is the number of channels of the input image.…”
Section: Network Architecturementioning
confidence: 99%
“…There are three parameters to be optimized in (5), namely W, b and v. We use the control variable method to optimize the three parameters and obtain better network model parameters finally. The algorithm of SPHDCNN is shown in Algorithm 2.…”
Section: Sphdcnn Implementationmentioning
confidence: 99%
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