Weakly-supervised land classification for coastal zone based on deep convolutional neural networks by incorporating dual-polarimetric characteristics into training dataset

TL;DR

This paper investigates weakly-supervised land classification in coastal zones using deep convolutional neural networks trained on dual-polarimetric PolSAR data, introducing a novel dataset creation method and transfer learning strategies.

Contribution

It presents a new approach to generate training datasets with enhanced supervision from PolSAR data and evaluates multiple DCNN models for improved land classification accuracy.

Findings

Effective training dataset creation method for PolSAR data.

Improved land classification accuracy with transfer learning.

Comparison of SegNet, U-Net, and LinkNet performance.

Abstract

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.