  In this work we explore the performance of DCNNs on semantic segmentation
using spaceborne polarimetric synthetic aperture radar (PolSAR) datasets. The
semantic segmentation task using PolSAR data can be categorized as weakly
supervised learning when the characteristics of SAR data and data annotating
procedures are factored in. Datasets are initially analyzed for selecting
feasible pre-training images. Then the differences between spaceborne and
airborne datasets are examined in terms of spatial resolution and viewing
geometry. In this study we used two dual-polarimetric images acquired by
TerraSAR-X DLR. A novel method to produce training dataset with more supervised
information is developed. Specifically, a series of typical classified images
as well as intensity images serve as training datasets. A field survey is
conducted for an area of about 20 square kilometers to obtain a ground truth
dataset used for accuracy evaluation. Several transfer learning strategies are
made for aforementioned training datasets which will be combined in a
practicable order. Three DCNN models, including SegNet, U-Net, and LinkNet, are
implemented next.
