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ADS-Net:An Attention-Based deeply supervised network for remote sensing image change detection
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2021-04-30 , DOI: 10.1016/j.jag.2021.102348
Decheng Wang , Xiangning Chen , Mingyong Jiang , Shuhan Du , Bijie Xu , Junda Wang

Change detection technology is an important key to analyze remote sensing data and is of great significance for accurate comprehension of the earth's surface changes. With the continuous development and progress of deep learning technology, fully convolutional neural networks are applied gradually in remote sensing change detection tasks. The present methods mainly encounter the problems of simple network structure, poor detection of small change areas, and poor robustness since they cannot completely obtain the relationships and differences between the features of bi-temporal images. To solve such problems, we propose an attention mechanism-based deep supervision network (ADS-Net) for the change detection of bi-temporal remote sensing images. First, an encoding–decoding full convolutional network is designed with a dual-stream structure. Various level features of bi-temporal images are extracted in the encoding stage, then in the decoding stage, feature maps of different levels are inserted into a deep supervision network with different branches to reconstruct the change map. Ultimately, to obtain the final change detection map, the prediction results of each branch in the deep supervision network are fused with various weights. To highlight the characteristics of change, we propose an adaptive attention mechanism combining spatial and channel features to capture the relationship of different scale changes and achieve more accurate change detection. ADS-Net has been tested on the LEVIR-CD and SVCD datasets of challenging remote sensing image change detection. The results of quantitative analysis and qualitative comparison indicate that the ADS-Net method comprises better effectiveness and robustness compared to the other state-of-the-art change detection methods.



中文翻译:

ADS-Net:基于注意力的深度监控网络,用于遥感图像变化检测

变化检测技术是分析遥感数据的重要关键,对于准确理解地球表面变化具有重要意义。随着深度学习技术的不断发展和进步,全卷积神经网络逐渐应用于遥感变化检测任务中。由于这些方法不能完全获得双时相图像的特征之间的关系和差异,因此主要遇到网络结构简单,对小变化区域的检测较差以及鲁棒性较差的问题。为了解决这些问题,我们提出了一种基于注意力机制的深度监督网络(ADS-Net),用于双时相遥感图像的变化检测。首先,采用双流结构设计编码-解码全卷积网络。在编码阶段提取双时相图像的各个级别特征,然后在解码阶段,将不同级别的特征图插入具有不同分支的深度监管网络中,以重建变化图。最终,为了获得最终的变化检测图,将深度监控网络中每个分支的预测结果与各种权重融合在一起。为了突出变化的特征,我们提出了一种结合空间和通道特征的自适应注意力机制,以捕获不同尺度变化的关系,并实现更准确的变化检测。ADS-Net已在具有挑战性的遥感影像变化检测的LEVIR-CD和SVCD数据集上进行了测试。

更新日期:2021-05-02
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