Land-cover classification with high-resolution remote sensing images using transferable deep models
两个问题： 1、 深度学习模型的可移植性 2、 缺乏注释良好的大型陆地覆盖数据集 **主要：**使用patch来进行训练。首先使用源数据预训练好一个ResNet50,之后使用伪标签和relevant sample retrieval的方法得到目标集的标签，并用来微调网络；分割、分类用投票法确定patches的类别。 方法：使用有标签的数据训练的深度模型来分辨无标签数据。 主要思想：深度卷积网络展示
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相关文章
- 1. Transferred Deep Learning-Based Change Detection in Remote Sensing Images
- 2. 论文：Remote Sensing Image Scene Classification 代码执行过程各种坑
- 3. 论文：Remote Sensing Image Scene Classification 代码执行过程各类坑
- 4. A NOVEL DEEP FEATURE FUSION NETWORK FOR REMOTE SENSING SCENE CLASSIFICATION（论文翻译）
- 5. FUSING DEEP LOCAL AND GLOBAL FEATURES FOR REMOTE SENSING IMAGE SCENE CLASSIFICATION阅读笔记
- 6. 场景分类综述——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