Hierarchical Weakly Supervised Learning for Residential Area Semantic Segmentation in Remote Sensing Images
Hierarchical Weakly Supervised Learning for Residential Area Semantic Segmentation in Remote Sensing Images
复制标题
遥感图像中住宅区语义分割的分层弱监督学习
DOI：
10.1109/lgrs.2019.2914490
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发表时间：
2020-01
影响因子：
4.8
通讯作者：
Chen Donghui
中科院分区：
文献类型：
--
作者：
Zhang Libao;Ma Jie;Lv Xinran;Chen Donghui
居民地分割是遥感领域最基本的任务之一。最近，基于全监督卷积神经网络（CNN）的方法在语义分割领域显示出优越性。然而，这些基于CNN的方法的一个严重问题是像素级注释昂贵且费力。本文提出了一种新型的分层弱监督学习（HWSL）方法来实现遥感图像像素级的语义分割。首先，提出了一种弱监督层次显着性分析，通过计算CNN中间层的梯度图来捕获特定于类的层次显着性图序列。然后，超像素和低秩矩阵恢复被引入到突出的共同的显着区域和融合类特定的显着地图与自适应权重。最后，对类间显著性图进行减法运算，生成层次化残差显著性图，实现居住区分割。通过对两组遥感数据的综合评价和与七种方法的比较，验证了HWSL模型的优越性。
Residential-area segmentation is one of the most fundamental tasks in the field of remote sensing. Recently, fully supervised convolutional neural network (CNN)-based methods have shown superiority in the field of semantic segmentation. However, a serious problem for those CNN-based methods is that pixel-level annotations are expensive and laborious. In this study, a novel hierarchical weakly supervised learning (HWSL) method is proposed to realize pixel-level semantic segmentation in remote sensing images. First, a weakly supervised hierarchical saliency analysis is proposed to capture a sequence of class-specific hierarchical saliency maps by computing the gradient maps with respect to the middle layers of the CNN. Then, superpixels and low-rank matrix recovery are introduced to highlight the common salient areas and fuse class-specific saliency maps with adaptive weights. Finally, a subtraction operation between class-specific saliency maps is conducted to generate hierarchical residual saliency maps and fulfill residential-area segmentation. Comprehensive evaluations with two remote sensing data sets and comparison with seven methods validate the superiority of the proposed HWSL model.
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DOI：
10.1109/cvpr.2009.5206596
发表时间：
2009-06
期刊：
2009 IEEE Conference on Computer Vision and Pattern Recognition
影响因子：
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作者：
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DOI：
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发表时间：
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影响因子：
5.5
作者：
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通讯作者：
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DOI：
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发表时间：
2017-04-01
影响因子：
23.6
作者：
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通讯作者：
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基于频域分析和显着区域检测的遥感图像感兴趣区域提取
影响因子：
4.8
作者：
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通讯作者：
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DOI：
10.1109/tpami.2017.2699184
发表时间：
2018-04-01
影响因子：
23.6
作者：
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通讯作者：
Yuille, Alan L.