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首页> 外文期刊>Industrial Informatics, IEEE Transactions on >Deep Learning and Superpixel Feature Extraction Based on Contractive Autoencoder for Change Detection in SAR Images
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Deep Learning and Superpixel Feature Extraction Based on Contractive Autoencoder for Change Detection in SAR Images

机译：基于压缩自动编码器的深度学习和超像素特征提取用于SAR图像变化检测

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摘要

Image segmentation based on superpixel is used in urban and land cover change detection for fast locating region of interest. However, the segmentation algorithms often degrade due to speckle noise in synthetic aperture radar images. In this paper, a feature learning method using a stacked contractive autoencoder (sCAE) is presented to extract the temporal change feature from superpixel with noise suppression. First, an affiliated temporal change image, which obtains temporal difference in the pixel level, are built by three different metrics. Second, the simple linear iterative clustering algorithm is used to generate superpixels, which tightly adhere to the change image boundaries for the purpose of acquiring homogeneous change samples. Third, a sCAE network is trained with the superpixel samples as input to learn the change features in semantic. Then, the encoded features by this sCAE model are binary classified to create the change result map. Finally, the proposed method is compared with methods based on principal components analysis and Markov random fields. Experiment results show that our deep learning model can separate nonlinear noise efficiently from change features and obtain better performance in change detection for synthetic aperture radar images than conventional change detection algorithms.
机译：基于超像素的图像分割用于城市和土地覆盖变化检测中，以快速定位感兴趣的区域。但是，由于合成孔径雷达图像中的斑点噪声，分割算法通常会降级。本文提出了一种使用堆叠收缩自编码器（sCAE）的特征学习方法，通过降噪从超像素中提取时间变化特征。首先，通过三个不同的度量来建立获得像素水平的时间差异的附属时间变化图像。其次，使用简单的线性迭代聚类算法生成超像素，该超像素紧密附着于变化图像边界，以获取均匀的变化样本。第三，使用超像素样本作为输入来训练sCAE网络，以学习语义上的变化特征。然后，对该sCAE模型编码的特征进行二进制分类以创建更改结果图。最后，将该方法与基于主成分分析和马尔可夫随机场的方法进行了比较。实验结果表明，与传统的变化检测算法相比，我们的深度学习模型可以有效地将非线性噪声与变化特征区分开，并在合成孔径雷达图像的变化检测中获得更好的性能。

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