Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network
AbstractAs a variant of Convolutional Neural Networks (CNNs) in Deep Learning, the Fully Convolutional Network (FCN) model achieved state-of-the-art performance for natural image semantic segmentation. In this paper, an accurate classification approach for high resolution remote sensing imagery based on the improved FCN model is proposed. Firstly, we improve the density of output class maps by introducing Atrous convolution, and secondly, we design a multi-scale network architecture by adding a skip-layer structure to make it capable for multi-resolution image classification. Finally, we further refine the output class map using Conditional Random Fields (CRFs) post-processing. Our classification model is trained on 70 GF-2 true color images, and tested on the other 4 GF-2 images and 3 IKONOS true color images. We also employ object-oriented classification, patch-based CNN classification, and the FCN-8s approach on the same images for comparison. The experiments show that compared with the existing approaches, our approach has an obvious improvement in accuracy. The average precision, recall, and Kappa coefficient of our approach are 0.81, 0.78, and 0.83, respectively. The experiments also prove that our approach has strong applicability for multi-resolution image classification. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Fu, G.; Liu, C.; Zhou, R.; Sun, T.; Zhang, Q. Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network. Remote Sens. 2017, 9, 498.
Fu G, Liu C, Zhou R, Sun T, Zhang Q. Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network. Remote Sensing. 2017; 9(5):498.Chicago/Turabian Style
Fu, Gang; Liu, Changjun; Zhou, Rong; Sun, Tao; Zhang, Qijian. 2017. "Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network." Remote Sens. 9, no. 5: 498.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.