  Efficient and accurate polarimetric synthetic aperture radar (PolSAR) image
classification with a limited number of prior labels is always full of
challenges. For general supervised deep learning classification algorithms, the
pixel-by-pixel algorithm achieves precise yet inefficient classification with a
small number of labeled pixels, whereas the pixel mapping algorithm achieves
efficient yet edge-rough classification with more prior labels required. To
take efficiency, accuracy and prior labels into account, we propose a novel
pixel-refining parallel mapping network in the complex domain named CRPM-Net
and the corresponding training algorithm for PolSAR image classification.
CRPM-Net consists of two parallel sub-networks: a) A transfer dilated
convolution mapping network in the complex domain (C-Dilated CNN) activated by
a complex cross-convolution neural network (Cs-CNN), which is aiming at precise
localization, high efficiency and the full use of phase information; b) A
complex domain encoder-decoder network connected parallelly with C-Dilated CNN,
which is to extract more contextual semantic features. Finally, we design a
two-step algorithm to train the Cs-CNN and CRPM-Net with a small number of
labeled pixels for higher accuracy by refining misclassified labeled pixels. We
verify the proposed method on AIRSAR and E-SAR datasets. The experimental
results demonstrate that CRPM-Net achieves the best classification results and
substantially outperforms some latest state-of-the-art approaches in both
efficiency and accuracy for PolSAR image classification. The source code and
trained models for CRPM-Net is available at:
https://github.com/PROoshio/CRPM-Net.
