In order to automatically extract tailing ponds in North China, the SSD target detection model based on deep learning is applied to extract tailing ponds from remote sensing images. Firstly, 2 000 samples in North China were labeled as the foundation of database. 1 500 samples were randomly selected as training samples, and the remaining samples were used as test samples to verify the detection accuracy of the model. By using the original SSD model, the targets of large tailing ponds can not be detected accurately. In this work the relationship between the corresponding receptive field of convolution layer and the size of tailing reservoir in image was analyzed. Moreover, in order to improve the detection accuracy of the model for large-scale tailing pond targets, we modified the structure of SSD model by introducing an extra convolution layer. Experiments show that, compared with the original SSD model, the modified SSD model improves the detection accuracy by 10% and the recall rate by 14.4% at 0.3 confidence level. The detection accuracy for large tailing ponds is also improved. In this work, the feasibility of automatic extraction of tailing reservoir based on deep learning SSD model and the effectiveness of the modified algorithm were verified.
YAN Kai
,
SHEN Ting
,
CHEN Zhengchao
,
YAN Hongxuan
. Automatic extraction of tailing pond based on SSD of deep learning[J]. Journal of University of Chinese Academy of Sciences, 2020
, 37(3)
: 360
-367
.
DOI: 10.7523/j.issn.2095-6134.2020.03.009
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