Extraction of steel plants based on optimized SSD network incorporating negative sample's multi channels

doi:10.7523/j.issn.2095-6134.2020.03.008

›› 2020, Vol. 37 ›› Issue (3): 352-359.DOI: 10.7523/j.issn.2095-6134.2020.03.008

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Extraction of steel plants based on optimized SSD network incorporating negative sample's multi channels

LU Kaixuan^1,2, LI Guoqing³, CHEN Zhengchao¹, ZAN Luyang^1,2, LI Baipeng¹, GAO Jianwei¹

1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;
2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;
3. Institute of Remote Sensing and Surveying and Mapping Henan, Zhengzhou 450003, China

Received:2019-01-23 Revised:2019-03-20 Online:2020-05-15

Abstract

Abstract: It is important to accurately detect steel plants for capacity reduction monitoring and environmental protection. The traditional method is time-consuming and laborious, and can not be used to monitor the steel plants in large areas. We propose a stable and accurate method by adding a maxout module to SSD, namely, transforming the negative sample path into a multi-branch structure. The neural network learns abundant features of hard negative samples, and thereby increases resistance to the useless features. Meanwhile, we used the well-trained model to detect steel plants in the Jing-Jin-Ji area based on GF-1 data. The results were compared with the data of the steel plants obtained from visual interpretation. Our method detects steel plants in the Jing-Jin-Ji area with an accuracy of more than 80%.

Key words: deep learning, GF-1 data, steel plant detection, Jing-Jin-Ji area, maxout module, object detection

CLC Number:

TP751

LU Kaixuan, LI Guoqing, CHEN Zhengchao, ZAN Luyang, LI Baipeng, GAO Jianwei. Extraction of steel plants based on optimized SSD network incorporating negative sample's multi channels[J]. , 2020, 37(3): 352-359.

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Extraction of steel plants based on optimized SSD network incorporating negative sample's multi channels

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