Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery
1.Introduction 2. Model 我們對原始的U-Net網絡進行了改進，使用了類似的網絡和類似的編碼器，使用預訓練的ResNet-34網絡作爲編碼器。卷積核的大小是7x7,stride=2,在每個residual block中，第一個卷積的步長是2，剩下的卷積操作步長都爲1。 3. Training 使用Jaccard index（IOU）作爲評價指標 離散公式：（分別爲像素i的 二
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相關文章
- 1. LiDAR Textbook & Automated Road Network Extraction
- 2. Road Extraction by Deep Residual U-Net
- 3. 3d fully convolutional network for vehicle detection in point cloud
- 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 簡要概述（未完工...）