A novel framework for remote sensing image scene classification    (Open Access)

作者：Jiang, Shulong; Zhao, Hongrui^*; Wu, Wenjia; Tan, Qifan

来源：2018 ISPRS TC III Mid-Term Symposium on Developments, Technologies and Applications in Remote Sensing, 2018-05-07 To 2018-05-10.

DOI：10.5194/isprs-archives-XLII-3-657-2018

• Convolutional neural network
• Convolutional Neural Networks (CNN)
• Fully-connected layers
• High resolution remote sensing
• Remote sensing images classification
• Scene classification
• Spatial distribution patterns
• XGBoost

High resolution remote sensing (HRRS) images scene classification aims to label an image with a specific semantic category. HRRS images contain more details of the ground objects and their spatial distribution patterns than low spatial resolution images. Scene classification can bridge the gap between low-level features and high-level semantics. It can be applied in urban planning, target detection and other fields. This paper proposes a novel framework for HRRS images scene classification. This framework combines the convolutional neural network (CNN) and XGBoost, which utilizes CNN as feature extractor and XGBoost as a classifier. Then, this framework is evaluated on two different HRRS images datasets: UC-Merced dataset and NWPU-RESISC45 dataset. Our framework achieved satisfying accuracies on two datasets, which is 95.57% and 83.35% respectively. From the experiments result, our framework has been proven to be effective for remote sensing images classification. Furthermore, we believe this framework will be more practical for further HRRS scene classification, since it costs less time on training stage.

• 出版日期2018
• 单位清华大学