计算机科学 ›› 2022, Vol. 49 ›› Issue (6): 193-198.doi: 10.11896/jsjkx.210500058
蓝凌翔, 池明旻
LAN Ling-xiang, CHI Ming-min
摘要： 变化检测是遥感的重要任务之一,通常被认为是像元级的分类问题。近年来,深度神经网络由于对双时相图像具有强大的层次表示能力,在变化检测中得到了广泛应用。文中基于编码器-融合-解码器框架,提出了一种基于特征注意力融合的变化检测网络(FFAN),其将编码器生成的特征与经由注意力机制增强后的双时相差异特征进行融合,以更好地捕获双时相变化信息。特别地,通过由注意力机制增强的双时相特征,可以显著增强深层网络中间层中变化信息的传播,该特征显式地建模双时相输入的相互依赖性并自适应地重新校准FFAN中的变化激活。在开源数据集上进行的实验表明,与现有方法相比,所提FFAN具有更优异的检测性能。
中图分类号:
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