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Current VHR(Very High Resolution) satellite images enable the detailed monitoring of the earth and can capture the ongoing works of railway construction. In this paper, we present an integrated framework applied to monitoring the railway construction in China, using QuickBird, GF-2 and Google Earth VHR satellite images. We also construct a novel DCNNs-based (Deep Convolutional Neural Networks) semantic segmentation network to label the temporary works such as borrow & spoil area, camp, beam yard and ESAs(Environmental Sensitive Areas) such as resident houses throughout the whole railway construction project using VHR satellite images. In addition, we employ HED edge detection sub-network to refine the boundary details and attention cross entropy loss function to fit the sample class disequilibrium problem. Our semantic segmentation network is trained on 572 VHR true color images, and tested on the 15 QuickBird true color images along Ruichang-Jiujiang railway during 2015-2017. The experiment results show that compared with the existing state-of-the-art approach, our approach has obvious improvements with an overall accuracy of more than 80%.
Rui Guo; Ronghua Liu; Na Li; Wei Liu. DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images. 2019, 1 .
AMA StyleRui Guo, Ronghua Liu, Na Li, Wei Liu. DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images. . 2019; ():1.
Chicago/Turabian StyleRui Guo; Ronghua Liu; Na Li; Wei Liu. 2019. "DV3+HED+: A DCNNs-based Framework to Monitor Temporary Works and ESAs in Railway Construction Project Using VHR Satellite Images." , no. : 1.
With the rapid development of satellite remote sensing technology, the size of image datasets in many application areas is growing exponentially and the demand for Land-Cover and Land-Use change remote sensing data is growing rapidly. It is thus becoming hard to efficiently and intelligently retrieve the change information that users need from massive image databases. In this paper, content-based image retrieval is successfully applied to change detection, and a content-based remote sensing image change information retrieval model is introduced. First, the construction of a new model framework for change information retrieval from a remote sensing database is described. Then, as the target content cannot be expressed by one kind of feature alone, a multiple-feature, integrated retrieval model is proposed. Thirdly, an experimental prototype system that was set up to demonstrate the validity and practicability of the model is described. The proposed model is a new method of acquiring change detection information from remote sensing imagery and so can reduce the need for image pre-processing and also deal with problems related to seasonal changes, as well as other problems encountered in the field of change detection. Meanwhile, the new model has important implications for improving remote sensing image management and autonomous information retrieval. The experiment results obtained using a Landsat data set show that the use of the new model can produce promising results. A coverage rate and mean average precision of 71% and 89%, respectively, were achieved for the top 20 returned pairs of images.
Caihong Ma; Wei Xia; Fu Chen; Jianbo Liu; Qin Dai; Liyuan Jiang; Jianbo Duan; Wei Liu. A Content-Based Remote Sensing Image Change Information Retrieval Model. ISPRS International Journal of Geo-Information 2017, 6, 310 .
AMA StyleCaihong Ma, Wei Xia, Fu Chen, Jianbo Liu, Qin Dai, Liyuan Jiang, Jianbo Duan, Wei Liu. A Content-Based Remote Sensing Image Change Information Retrieval Model. ISPRS International Journal of Geo-Information. 2017; 6 (10):310.
Chicago/Turabian StyleCaihong Ma; Wei Xia; Fu Chen; Jianbo Liu; Qin Dai; Liyuan Jiang; Jianbo Duan; Wei Liu. 2017. "A Content-Based Remote Sensing Image Change Information Retrieval Model." ISPRS International Journal of Geo-Information 6, no. 10: 310.
With the rapid development of satellite remote sensing technology, the volume of image datasets in many application areas is growing exponentially and the demand for Land-Cover and Land-Use change remote sensing data is growing rapidly. It is thus becoming hard to efficiently and intelligently retrieve the change information that users need from massive image databases. In this paper, content-based image retrieval is successfully applied to change detection and a content-based remote sensing image change information retrieval model is introduced. First, the construction of a new model framework for change information retrieval in a remote sensing database is described. Then, as the target content cannot be expressed by one kind of feature alone, a multiple-feature integrated retrieval model is proposed. Thirdly, an experimental prototype system that was set up to demonstrate the validity and practicability of the model is described. The proposed model is a new method of acquiring change detection information from remote sensing imagery and so can reduce the need for image pre-processing, deal with problems related toseasonal changes as well as other problems encountered in the field of change detection. Meanwhile, the new model has important implications for improving remote sensing image management and autonomous information retrieval.
Caihong Ma; Wei Xia; Fu Chen; Jianbo Liu; Qin Dai; Liyuan Jiang; Jianbo Duan; Wei Liu. A Content-Based Remote Sensing Image Change Information Retrieval Model. 2017, 1 .
AMA StyleCaihong Ma, Wei Xia, Fu Chen, Jianbo Liu, Qin Dai, Liyuan Jiang, Jianbo Duan, Wei Liu. A Content-Based Remote Sensing Image Change Information Retrieval Model. . 2017; ():1.
Chicago/Turabian StyleCaihong Ma; Wei Xia; Fu Chen; Jianbo Liu; Qin Dai; Liyuan Jiang; Jianbo Duan; Wei Liu. 2017. "A Content-Based Remote Sensing Image Change Information Retrieval Model." , no. : 1.
HY-2A, as the first Chinese ocean dynamic environment satellite, provides an effective and efficient way of observing ocean properties. However, in the operational stage, some inconveniences of the existing ground application system have appeared. Based on the review of users’ requirements for data services, the Customized Automatic Processing Framework (CAPF) for HY-2A advanced products is proposed and has been developed. As an extension of the existing ground application system, the framework provides interfaces for adding customized algorithms, designing on-demand processing workflows, and scheduling the processing procedures. With the customized processing templates, the framework allows users to easily process the products according to their own expectations, which facilitates the usage of HY-2A satellite advanced products.
Wei Liu; Shibin Liu; Lei Huang; Jianbo Duan; Jing Zhang; Xinpeng Li; Jianbo Liu. The Customized Automatic Processing Framework for HY-2A Satellite Marine Advanced Products. Remote Sensing 2016, 8, 1009 .
AMA StyleWei Liu, Shibin Liu, Lei Huang, Jianbo Duan, Jing Zhang, Xinpeng Li, Jianbo Liu. The Customized Automatic Processing Framework for HY-2A Satellite Marine Advanced Products. Remote Sensing. 2016; 8 (12):1009.
Chicago/Turabian StyleWei Liu; Shibin Liu; Lei Huang; Jianbo Duan; Jing Zhang; Xinpeng Li; Jianbo Liu. 2016. "The Customized Automatic Processing Framework for HY-2A Satellite Marine Advanced Products." Remote Sensing 8, no. 12: 1009.
Remote sensing (RS) images play a significant role in disaster emergency response. Web2.0 changes the way data are created, making it possible for the public to participate in scientific issues. In this paper, an experiment is designed to evaluate the reliability of crowdsourcing buildings collapse assessment in the early time after an earthquake based on aerial remote sensing image. The procedure of RS data pre-processing and crowdsourcing data collection is presented. A probabilistic model including maximum likelihood estimation (MLE), Bayes’ theorem and expectation-maximization (EM) algorithm are applied to quantitatively estimate the individual error-rate and “ground truth” according to multiple participants’ assessment results. An experimental area of Yushu earthquake is provided to present the results contributed by participants. Following the results, some discussion is provided regarding accuracy and variation among participants. The features of buildings labeled as the same damage type are found highly consistent. This suggests that the building damage assessment contributed by crowdsourcing can be treated as reliable samples. This study shows potential for a rapid building collapse assessment through crowdsourcing and quantitatively inferring “ground truth” according to crowdsourcing data in the early time after the earthquake based on aerial remote sensing image.
Shuai Xie; Jianbo Duan; Shibin Liu; Qin Dai; Wei Liu; Yong Ma; Rui Guo; Caihong Ma. Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake. Remote Sensing 2016, 8, 759 .
AMA StyleShuai Xie, Jianbo Duan, Shibin Liu, Qin Dai, Wei Liu, Yong Ma, Rui Guo, Caihong Ma. Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake. Remote Sensing. 2016; 8 (9):759.
Chicago/Turabian StyleShuai Xie; Jianbo Duan; Shibin Liu; Qin Dai; Wei Liu; Yong Ma; Rui Guo; Caihong Ma. 2016. "Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake." Remote Sensing 8, no. 9: 759.