计算机科学
人工智能
模式识别(心理学)
判别式
聚类分析
马尔可夫随机场
噪音(视频)
水准点(测量)
上下文图像分类
领域(数学)
合成孔径雷达
图像(数学)
图像分割
数学
地理
地图学
纯数学
作者
Si‐Han Yang,Haixia Bi,Xiaotian Wang,Danfeng Hong
标识
DOI:10.1109/igarss52108.2023.10281880
摘要
Due to the difficulty of obtaining manual annotations for polarimetric synthetic aperture radar (PolSAR) images, the problem of analyzing these images without or with few labels has become a current challenge. Considering the scarcity of labels, this paper proposes a noise-tolerant deep clustering-based PolSAR image classification that mainly uses autoencoders to learn discriminative features. In addition, in order to improve the performance and noise resistance of this method, we adopt Markov Random Field (MRF) to enhance the smoothness of class labels. We conducted experiments on a real benchmark PolSAR image, and the results show that our method achieves state-of-the-art PolSAR image classification results without any manual annotations.
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