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Open AccessArticle

Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks

1
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
2
University of Chinese Academy of Sciences, No. 19 (A) Yuquan Road, Shijingshan District, Beijing 100049, China
3
School of Econometrics and Management, University of the Chinese Academy of Sciences, No.19 (A) Yuquan Road, Shijingshan District, Beijing 100049, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(3), 110; https://doi.org/10.3390/ijgi7030110
Received: 8 January 2018 / Revised: 28 February 2018 / Accepted: 12 March 2018 / Published: 14 March 2018
Considering the classification of high spatial resolution remote sensing imagery, this paper presents a novel classification method for such imagery using deep neural networks. Deep learning methods, such as a fully convolutional network (FCN) model, achieve state-of-the-art performance in natural image semantic segmentation when provided with large-scale datasets and respective labels. To use data efficiently in the training stage, we first pre-segment training images and their labels into small patches as supplements of training data using graph-based segmentation and the selective search method. Subsequently, FCN with atrous convolution is used to perform pixel-wise classification. In the testing stage, post-processing with fully connected conditional random fields (CRFs) is used to refine results. Extensive experiments based on the Vaihingen dataset demonstrate that our method performs better than the reference state-of-the-art networks when applied to high-resolution remote sensing imagery classification. View Full-Text
Keywords: high resolution imagery; remote sensing; convolution neural network; semantic segmentation; data augmentation; deep learning high resolution imagery; remote sensing; convolution neural network; semantic segmentation; data augmentation; deep learning
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MDPI and ACS Style

Guo, R.; Liu, J.; Li, N.; Liu, S.; Chen, F.; Cheng, B.; Duan, J.; Li, X.; Ma, C. Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks. ISPRS Int. J. Geo-Inf. 2018, 7, 110.

AMA Style

Guo R, Liu J, Li N, Liu S, Chen F, Cheng B, Duan J, Li X, Ma C. Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks. ISPRS International Journal of Geo-Information. 2018; 7(3):110.

Chicago/Turabian Style

Guo, Rui; Liu, Jianbo; Li, Na; Liu, Shibin; Chen, Fu; Cheng, Bo; Duan, Jianbo; Li, Xinpeng; Ma, Caihong. 2018. "Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks" ISPRS Int. J. Geo-Inf. 7, no. 3: 110.

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