道路分割論文研讀之D-LinkNet ：LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Sate
本人最近在研究路網生成算法，遇到瓶頸，借鑑衛星圖識別的經典算法。 1.Introduction: 道路提取可視爲二值語義分割任務，像素級二分類。 衛星圖路網分割的挑戰： 1. 輸入的圖像是高分辨率的，因此網絡應該有大的接收範圍，可以覆蓋整個圖像。2. 衛星圖像中的道路往往細長複雜，覆蓋了整個圖像的一小部分。在這種情況下，保存詳細的空間信息非常重要。3. 道路具有天然的連通性和較大的跨度。考慮到道路
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相關文章
- 1. 論文解讀：D-LinkNet ：LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satelli
- 2. LinkNet with Pretrained Encoder and Dilated Convolution for HighResolution Satellite Image
- 3. 【圖像分割論文閱讀】LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
- 4. 【圖像分割論文閱讀】RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
- 5. 【圖像分割論文閱讀】DeepLabV3+:Encoder-Decoder with Atrous Separable Convolution for SemanticImageSegmentation
- 6. 語義分割--RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
- 7. RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation解讀
- 8. 論文閱讀筆記之——《Learning Pyramid-Context Encoder Network for High-Quality Image Inpainting》
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