中文引用格式： 任吉宏，劉暢. 基于自適應(yīng)超像素的少樣本極化SAR圖像特征增強(qiáng)方法研究[J].電子技術(shù)應(yīng)用，2022，48(10)：144-149. 英文引用格式： Ren Jihong, Liu Chang. An adaptive superpixel-based polarimetric feature enhancement method for polarimetric SAR image classification with limited labeled data[J]. Application of Electronic Technique，2022，48(10)：144-149.
An adaptive superpixel-based polarimetric feature enhancement method for polarimetric SAR image classification with limited labeled data
Ren Jihong1,2，Liu Chang1,2
1.Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China; 2.University of Chinese Academy of Sciences, Beijing 100049, China
Abstract： The performance of supervised Polarimetric Synthetic Aperture Radar (PolSAR) image terrain classification heavily relies on ground-truth samples, which could be a problem when the sample size is small or few labels are imprecise. Since PolSAR image has spatial and spectral information redundancy, spatial neighborhood information can improve the discriminative and robustness of sample features. In this paper, a polarimetric feature enhancement method is proposed for improving the robustness of data representation. With the help of a statistical polarimetric HSV color space pseudo-color image generation method and an adaptive superpixel clustering algorithm, the enhanced feature of each sample can be obtained from both the original sample feature and its corresponding superpixel. Experiments with the benchmark datasets show that the proposed method can improve the robustness and accuracy of classification results with a small size of ground-truth samples.
