基于复合损失函数的卫星图像建筑物检测

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2018-06-01 DOI:10.1109/CVPRW.2018.00040

Sergey Golovanov, R. Kurbanov, A. Artamonov, A. Davydow, S. Nikolenko

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引用次数: 19

摘要

在本文中，我们提出了一个基于linknet的架构，其中包含SE-ResNeXt-50编码器和一种新的训练策略，该策略强烈依赖于图像预处理并结合扭曲的网络输出。该架构结合了预训练的卷积编码器和对称扩展路径，可以实现精确定位。我们表明，这样的网络可以在具有复合损失函数的普通RGB图像上进行训练，并在DeepGlobe挑战中从卫星图像中提取建筑物的结果具有竞争力

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Building Detection from Satellite Imagery Using a Composite Loss Function

In this paper, we present a LinkNet-based architecture with SE-ResNeXt-50 encoder and a novel training strategy that strongly relies on image preprocessing and incorporating distorted network outputs. The architecture combines a pre-trained convolutional encoder and a symmetric expanding path that enables precise localization. We show that such a network can be trained on plain RGB images with a composite loss function and achieves competitive results on the DeepGlobe challenge on building extraction from satellite images

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来源期刊

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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