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DA-RoadNet: A Dual-Attention Network for Road Extraction From High Resolution Satellite Imagery
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( IF 5.5 ) Pub Date : 2021-05-25 , DOI: 10.1109/jstars.2021.3083055
Jie Wan , Zhong Xie , Yongyang Xu , Siqiong Chen , Qinjun Qiu

Recent advances in deep-learning methods have shown extraordinary performance in road extraction from high resolution satellite imagery. However, most existing deep-learning network models yield discontinuous and incomplete results because of shadows and occlusions. To address this problem, a dual-attention road extraction network (DA-RoadNet) with a certain semantic reasoning ability is proposed. First, DA-RoadNet is designed based on a shallow encoder-to-decoder network with densely connected blocks, which can minimize the loss of road structure information caused by multiple down-sampling operations. Moreover, by constructing a novel attention mechanism module, the proposed network is able to explore and integrate the invisible correlations among road features with their global dependency in spatial and channel dimension respectively. Finally, considering that the proportion of road samples is small in the satellite imagery, a hybrid loss function is appended to handle class imbalance, which enables the network model to train stablely and avoid local optimum. The validation experiments using two open road datasets demonstrate that the proposed DA-RoadNet can effectively solve discontinuous problems and preserve integrity of the extracted roads, thus resulting in a higher accuracy of road extraction compared with other developed state-of-the-arts. The considerable performance on the two challenging benchmarks also proves the powerful generation ability of our method.

中文翻译:

DA-RoadNet:用于从高分辨率卫星图像中提取道路的双注意网络

深度学习方法的最新进展在从高分辨率卫星图像中提取道路方面表现出非凡的性能。然而,由于阴影和遮挡,大多数现有的深度学习网络模型会产生不连续和不完整的结果。针对这个问题,提出了一种具有一定语义推理能力的双注意力道路提取网络(DA-RoadNet)。首先,DA-RoadNet 是基于具有密集连接块的浅层编码器到解码器网络设计的,可以最大限度地减少多次下采样操作造成的道路结构信息丢失。此外,通过构建一个新的注意力机制模块,所提出的网络能够分别探索和整合道路特征之间的不可见相关性及其在空间和通道维度上的全局依赖性。最后,考虑到卫星图像中道路样本的比例较小,附加了混合损失函数来处理类不平衡,使网络模型能够稳定训练,避免局部最优。使用两个开放道路数据集的验证实验表明,所提出的 DA-RoadNet 可以有效解决不连续问题并保持提取道路的完整性,从而与其他已开发的最新技术相比,道路提取的准确性更高。在两个具有挑战性的基准测试中的可观表现也证明了我们方法的强大生成能力。使用两个开放道路数据集的验证实验表明,所提出的 DA-RoadNet 可以有效解决不连续问题并保持提取道路的完整性,从而与其他已开发的最新技术相比,道路提取的准确性更高。在两个具有挑战性的基准测试中的可观表现也证明了我们方法的强大生成能力。使用两个开放道路数据集的验证实验表明,所提出的 DA-RoadNet 可以有效解决不连续问题并保持提取道路的完整性,从而与其他已开发的最新技术相比,道路提取的准确性更高。在两个具有挑战性的基准测试中的可观表现也证明了我们方法的强大生成能力。
更新日期:2021-07-06
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