pixel-wise classification method for high resolution remote sensing imagery using deep neural networks

Clicks: 227
ID: 204149
2018
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Abstract
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.
Reference Key
guo2018isprspixel-wise Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Rui Guo;Jianbo Liu;Na Li;Shibin Liu;Fu Chen;Bo Cheng;Jianbo Duan;Xinpeng Li;Caihong Ma
Journal población y desarrollo
Year 2018
DOI
10.3390/ijgi7030110
URL
http://www.mdpi.com/2220-9964/7/3/110
Keywords
Convolution neural network remote sensing semantic segmentation data augmentation

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