Cross-domain road detection based on global-local adversarial learning framework from very high resolution satellite imagery
Authors:
- Xiaoyan LuXLXiaoyan Lu
- Yanfei ZhongYZYanfei Zhong
- Zhuo ZhengZZZhuo Zheng
- Junjue WangJWJunjue Wang
- 1 October 2021
- journal article
- research article
- Published by Elsevier in ISPRS Journal of Photogrammetry and Remote Sensing
- Vol. 180, 296-312
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (41771385)
- National Natural Science Foundation of China (42071350)
This publication has 21 references indexed in Scilit:
- Introduction to PyTorch2017
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2017
- A Unified Framework for Metric Transfer LearningIEEE Transactions on Knowledge and Data Engineering, 2017
- Road Extraction From Very High Resolution Remote Sensing Optical Images Based on Texture Analysis and Beamlet TransformIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015
- An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed ImageryIEEE Transactions on Geoscience and Remote Sensing, 2012
- Semi-automatic road tracking by template matching and distance transformation in urban areasInternational Journal of Remote Sensing, 2011
- Large-Scale Machine Learning with Stochastic Gradient Descent2010
- Road Network Extraction and Intersection Detection From Aerial Images by Tracking Road FootprintsIEEE Transactions on Geoscience and Remote Sensing, 2007
- Tri-training: exploiting unlabeled data using three classifiersIEEE Transactions on Knowledge and Data Engineering, 2005
- The line segment match method for extracting road network from high-resolution satellite imagesIEEE Transactions on Geoscience and Remote Sensing, 2002