Synthetic aperture radar (SAR) image segmentation aims at generating homogeneous regions from a pixel-based image and is the basis of image interpretation. However, most of the existing segmentation methods usually neglect the appearance and spatial consistency during feature extraction and also require a large number of training data. In addition, pixel-based processing cannot meet the real time requirement. We hereby present a weakly supervised algorithm to perform the task of segmentation for high-resolution SAR images. For effective segmentation, the input image is first over-segmented into a set of primitive superpixels. This algorithm combines hierarchical conditional generative adversarial nets (CGAN) and conditional random fields (CRF). The CGAN-based networks can leverage abundant unlabeled data learning parameters, reducing their reliance on the labeled samples. In order to preserve neighborhood consistency in the feature extraction stage, the hierarchical CGAN is composed of two sub-networks, which are employed to extract the information of the central superpixels and the corresponding background superpixels, respectively. Afterwards, CRF is utilized to perform label optimization using the concatenated features. Quantified experiments on an airborne SAR image dataset prove that the proposed method can effectively learn feature representations and achieve competitive accuracy to the state-of-the-art segmentation approaches. More specifically, our algorithm has a higher Cohen's kappa coefficient and overall accuracy. Its computation time is less than the current mainstream pixel-level semantic segmentation networks.
Bibliographical noteFunding Information:
The SAR images used in the experiments are the courtesy of Beijing Institute of Radio Measurement, the authors would like to thank them for their support in this work. This research was funded by the National Natural Science Foundation of China, grant nos. 61771027, 61071139, 61471019, 61501011, and 61171122. Fei Ma was supported by Academic Excellence Foundation of BUAA for PhD Students. H. Zhou was supported by UK EPSRC under Grant EP/N011074/1, Royal Society-Newton Advanced Fellowship under Grant NA160342, and European Union's Horizon 2020 research and innovation program under the Marie-Sklodowska-Curie grant agreement no. 720325. Professor A. Hussain was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) grant no. EP/M026981/1.
© 2019 by the authors.
Copyright 2019 Elsevier B.V., All rights reserved.
- Conditional generative adversarial nets (CGAN)
- Conditional random fields (CRF)
- Neighborhood consistency
- Synthetic aperture radar (SAR)
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)