Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network

By Gang Fu, Changjun Liu, Rong Zhou. Remote Sensing, 2017

329 citations recorded by the source corpus.

As 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.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/3c7aea54b7c7321445204fc5078f7346388879f6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Zhong Xie
2. Yongyang Xu
3. Lichuan Gu
4. Xiaohui Yuan
5. Jianfang Shi
6. Zhanlong Chen
7. Liang Wu

In Media Technology

1. Zhong Xie
2. Yongyang Xu
3. Lichuan Gu
4. Xiaohui Yuan
5. Jianfang Shi
6. Zhanlong Chen
7. Liang Wu

Citations in the corpus

• Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters (2018, methodology): “The improved FCN model, which is designed as a multi-scale network architecture by adding a skip-layer structure, was trained to perform state-of-the-art natural image semantic segmentation [31].”
• A review of deep learning methods for semantic segmentation of remote sensing imagery (2021, methodology): “…of deep learning methods for segmenting remote sensing images inherit cross-entropy as the loss function (Alshehhi et al., 2017; Cheng et al., 2017; Fu et al., 2017; Kussul et al., 2017; Li, Zhang, & Shen, 2017; Wang et al., 2017; Yu, Jia, & Xu, 2017; Chen et al., 2018; Chen et al., 2018; Chen et…”