  Convolutional neural networks (CNNs) have been widely used to improve the
accuracy of polarimetric synthetic aperture radar (PolSAR) image
classification. However, in most studies, the difference between PolSAR images
and optical images is rarely considered. Most of the existing CNNs are not
tailored for the task of PolSAR image classification, in which complex-valued
PolSAR data have been simply equated to real-valued data to fit the optical
image processing architectures and avoid complex-valued operations. This is one
of the reasons CNNs unable to perform their full capabilities in PolSAR
classification. To solve the above problem, the objective of this paper is to
develop a tailored CNN framework for PolSAR image classification, which can be
implemented from two aspects: Seeking a better form of PolSAR data as the input
of CNNs and building matched CNN architectures based on the proposed input
form. In this paper, considering the properties of complex-valued numbers,
amplitude and phase of complex-valued PolSAR data are extracted as the input
for the first time to maintain the integrity of original information while
avoiding immature complex-valued operations. Then, a multi-task CNN (MCNN)
architecture is proposed to match the improved input form and achieve better
classification results. Furthermore, depthwise separable convolution is
introduced to the proposed architecture in order to better extract information
from the phase information. Experiments on three PolSAR benchmark datasets not
only prove that using amplitude and phase as the input do contribute to the
improvement of PolSAR classification, but also verify the adaptability between
the improved input form and the well-designed architectures.
