Land-cover classification with high-resolution remote sensing images using transferable deep models
兩個問題： 1、 深度學習模型的可移植性 2、 缺乏註釋良好的大型陸地覆蓋數據集 **主要：**使用patch來進行訓練。首先使用源數據預訓練好一個ResNet50,之後使用僞標籤和relevant sample retrieval的方法得到目標集的標籤，並用來微調網絡；分割、分類用投票法確定patches的類別。 方法：使用有標籤的數據訓練的深度模型來分辨無標籤數據。 主要思想：深度卷積網絡展示
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相關文章
- 1. Transferred Deep Learning-Based Change Detection in Remote Sensing Images
- 2. 論文：Remote Sensing Image Scene Classification 代碼執行過程各種坑
- 3. 論文：Remote Sensing Image Scene Classification 代碼執行過程各類坑
- 4. A NOVEL DEEP FEATURE FUSION NETWORK FOR REMOTE SENSING SCENE CLASSIFICATION（論文翻譯）
- 5. FUSING DEEP LOCAL AND GLOBAL FEATURES FOR REMOTE SENSING IMAGE SCENE CLASSIFICATION閱讀筆記
- 6. 場景分類綜述——Remote Sensing Image Scene Classification Meets Deep Learning
- 7. Nested Network with Two-Stream Pyramid for Salient Object Detection in Optical Remote Sensing Images
- 8. Learning Transferable Features with Deep Adaptation Networks
- 9. Deep Learning Models on Kubernetes with GPUs
- 10. Missing Data Reconstruction in Remote Sensing Image With a Unified Spatial–Temporal–Spectral Deep Co