  Convolutional neural networks (CNNs) have been applied to learn spatial
features for high-resolution (HR) synthetic aperture radar (SAR) image
classification. However, there has been little work on integrating the unique
statistical distributions of SAR images which can reveal physical properties of
terrain objects, into CNNs in a supervised feature learning framework. To
address this problem, a novel end-to-end supervised classification method is
proposed for HR SAR images by considering both spatial context and statistical
features. First, to extract more effective spatial features from SAR images, a
new deep spatial context encoder network (DSCEN) is proposed, which is a
lightweight structure and can be effectively trained with a small number of
samples. Meanwhile, to enhance the diversity of statistics, the nonstationary
joint statistical model (NS-JSM) is adopted to form the global statistical
features. Specifically, SAR images are transformed into the Gabor wavelet
domain and the produced multi-subbands magnitudes and phases are modeled by the
log-normal and uniform distribution. The covariance matrix is further utilized
to capture the inter-scale and intra-scale nonstationary correlation between
the statistical subbands and make the joint statistical features more compact
and distinguishable. Considering complementary advantages, a feature fusion
network (Fusion-Net) base on group compression and smooth normalization is
constructed to embed the statistical features into the spatial features and
optimize the fusion feature representation. As a result, our model can learn
the discriminative features and improve the final classification performance.
Experiments on four HR SAR images validate the superiority of the proposed
method over other related algorithms.
