计算机科学
分割
人工智能
鉴别器
图像翻译
图像分割
模式识别(心理学)
发电机(电路理论)
计算机视觉
图像(数学)
量子力学
电信
探测器
物理
功率(物理)
作者
Mi Zhang,Xiangyun Hu,Like Zhao,Shiyan Pang,Jinqi Gong,Min Luo
标识
DOI:10.1117/1.jrs.11.042622
摘要
Semantic segmentation has recently made rapid progress in the field of remote sensing and computer vision. However, many leading approaches cannot simultaneously translate label maps to possible source images with a limited number of training images. The core issue is insufficient adversarial information to interpret the inverse process and proper objective loss function to overcome the vanishing gradient problem. We propose the use of conditional least squares generative adversarial networks (CLS-GAN) to delineate visual objects and solve these problems. We trained the CLS-GAN network for semantic segmentation to discriminate dense prediction information either from training images or generative networks. We show that the optimal objective function of CLS-GAN is a special class of f-divergence and yields a generator that lies on the decision boundary of discriminator that reduces possible vanished gradient. We also demonstrate the effectiveness of the proposed architecture at translating images from label maps in the learning process. Experiments on a limited number of high resolution images, including close-range and remote sensing datasets, indicate that the proposed method leads to the improved semantic segmentation accuracy and can simultaneously generate high quality images from label maps.
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