Building segmentation in high-resolution InSAR images is a challenging task that can be useful for large-scale surveillance. Although complex-valued deep learning networks perform better than their real-valued counterparts for complex-valued SAR data, phase information is not retained throughout the network, which causes a loss of information. This paper proposes a Fully Complex-valued, Fully Convolutional Multi-feature Fusion Network(FC2MFN) for building semantic segmentation on InSAR images using a novel, fully complex-valued learning scheme. The network learns multi-scale features, performs multi-feature fusion, and has a complex-valued output. For the particularity of complex-valued InSAR data, a new complex-valued pooling layer is proposed that compares complex numbers considering their magnitude and phase. This helps the network retain the phase information even through the pooling layer. Experimental results on the simulated InSAR dataset show that FC2MFN achieves better results compared to other state-of-the-art methods in terms of segmentation performance and model complexity.
翻译:高分辨率 InSAR 图像中的建筑分隔是一项具有挑战性的任务,可用于大规模监测。尽管价值复杂的深层学习网络在价值复杂的合成合成孔径雷达数据方面的表现优于其实际价值的对等网络,但在整个网络中并没有保留阶段信息,从而造成信息损失。本文件建议建立一个全复杂、全革命性多功能融合网络(FC2MFN),用于利用一种新颖的、完全复杂价值的学习计划,在合成孔径雷达图像上建立语义分隔。该网络学习多种规模的特征,进行多功能融合,并具有复杂价值的输出。就复杂价值的合成孔径雷达数据的特殊性而言,建议建立一个新的复杂价值集合层,以比较复杂数量,同时考虑到其规模和阶段。这有助于网络通过集合层保留阶段信息。模拟的合成合成合成孔径雷达数据集的实验结果表明,在分解性表现和模型复杂性方面,FC2MMN与其他最先进的方法相比,取得了更好的结果。