Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery
1.Introduction 2. Model 我们对原始的U-Net网络进行了改进，使用了类似的网络和类似的编码器，使用预训练的ResNet-34网络作为编码器。卷积核的大小是7x7,stride=2,在每个residual block中，第一个卷积的步长是2，剩下的卷积操作步长都为1。 3. Training 使用Jaccard index（IOU）作为评价指标 离散公式：（分别为像素i的 二
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相关文章
- 1. LiDAR Textbook & Automated Road Network Extraction
- 2. Road Extraction by Deep Residual U-Net
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- 4. NR-SIQA Using Convolutional Neural Network for Adaptive Feature Extraction
- 5. Multi-task Fully Convolutional Network for Brain Tumour Segmentation
- 6. Attention-based Extraction of Structured Information from Street View Imagery
- 7. Transposed convolution & Fully Convolutional Neural Network
- 8. Fully Convolutional Adaptation Networks for Semantic Segmentation
- 9. Automatic skin lesion segmentation with fully convolutional-deconvolutional networks
- 10. Classification of Hyperspectral Imagery Using a New Fully Convolutional Neural Network 简要概述（未完工...）