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Gang Cheng
Basic Info . Basic Info is private. Honors and Awards . The user has no records in this section Career Timeline . The user has no records in this section. Short Biography . The user biography is not available. Following Followers Co-Authors The list of users this user is following is empty. Following: 0 users The user has no followers. Followers: 0 users Bing Zhang University of Chinese Academ... Liwei Li The Key Laboratory of Digita... Lianru Gao Key Laboratory of Digital Ea... Cheng Wang Laboratory of Digital Earth ... Qian Shen Key Laboratory of Digital Ea... Hongtao Wang School of Surveying and Land... Shuangting Wang School of Surveying and Land... Jinming Zhu School of Surveying and Land... Zongze Zhao School of Surveying and Land... Zhi Yan The Key Laboratory of Digita... Co-Authors: 10 users View all Feed . Journal article Detecting High-Rise Buildings from Sentinel-2 Data Based on Deep Learning Method Liwei Li Jinming Zhu Gang Cheng Bing Zhang https://doi.org/10.3390/rs13204073 Published: 12 October 2021 in Remote Sensing . Reads? 0 Downloads? 0 Abstract Cite All recommendations High-rise buildings (HRBs) as a modern and visually distinctive land use play an important role in urbanization. Large-scale monitoring of HRBs is valuable in urban planning and environmental protection and so on. Due to the complex 3D structure and seasonal dynamic image features of HRBs, it is still challenging to monitor large-scale HRBs in a routine way. This paper extends our previous work on the use of the Fully Convolutional Networks (FCN) model to extract HRBs from Sentinel-2 data by studying the influence of seasonal and spatial factors on the performance of the FCN model. 16 Sentinel-2 subset images covering four diverse regions in four seasons were selected for training and validation. Our results indicate the performance of the FCN-based method at the extraction of HRBs from Sentinel-2 data fluctuates among seasons and regions. The seasonal change of accuracy is larger than that of the regional change. If an optimal season can be chosen to get a yearly best result, F1 score of detected HRBs can reach above 0.75 for all regions with most errors located on the boundary of HRBs. FCN model can be trained on seasonally and regionally combined samples to achieve similar or even better overall accuracy than that of the model trained on an optimal combination of season and region. Uncertainties exist on the boundary of detected results and may be relieved by revising the definition of HRBs in a more rigorous way. On the whole, the FCN based method can be largely effective at the extraction of HRBs from Sentinel-2 data in regions with a large diversity in culture, latitude, and landscape. Our results support the possibility to build a powerful FCN model on a larger size of training samples for operational monitoring HRBs at the regional level or even on a country scale. ACS Style Liwei Li; Jinming Zhu; Gang Cheng; Bing Zhang. Detecting High-Rise Buildings from Sentinel-2 Data Based on Deep Learning Method. Remote Sensing 2021 , 13 , 4073 . AMA Style Liwei Li, Jinming Zhu, Gang Cheng, Bing Zhang. Detecting High-Rise Buildings from Sentinel-2 Data Based on Deep Learning Method. Remote Sensing. 2021; 13 (20):4073. Chicago/Turabian Style Liwei Li; Jinming Zhu; Gang Cheng; Bing Zhang. 2021. "Detecting High-Rise Buildings from Sentinel-2 Data Based on Deep Learning Method." Remote Sensing 13, no. 20: 4073. Journal article City Grade Classification Based on Connectivity Analysis by Luojia I Night-Time Light Images in Henan Province, China Zongze Zhao Gang Cheng Cheng Wang Shuangting Wang Hongtao Wang https://doi.org/10.3390/rs12111705 Published: 27 May 2020 in Remote Sensing . Reads? 0 Downloads? 0 Abstract Cite All recommendations City classification can provide important data and technical support for city planning and government decision-making. Traditional city classification mainly relies on the accumulation and analysis of census data, which requires a large time period and relies heavily on historical and statistical data. This paper mainly utilizes Luojia I Night-Time Light (NTL) images to analyze the rank classification of cities in Henan Province, China. Intensity values can be expressed as the mathematical surface of continuous human activities, and the basic characteristics of urban structures are determined by analogy with the topography of the earth. A connectivity analysis method for NTL images is proposed to analyze the connected regions of images at different intensity levels. By constructing a tree structure, different cities can be analyzed “crosswise” and “lengthwise” to generate a series of parametric information from connected regions of NTL images. Based on these parameters, 18 cities in Henan Province were classified and analyzed. The results show that these attribute information can be well used for city center detection and grade classification, and can meet the requirements of application analysis. ACS Style Zongze Zhao; Gang Cheng; Cheng Wang; Shuangting Wang; Hongtao Wang. City Grade Classification Based on Connectivity Analysis by Luojia I Night-Time Light Images in Henan Province, China. Remote Sensing 2020 , 12 , 1705 . AMA Style Zongze Zhao, Gang Cheng, Cheng Wang, Shuangting Wang, Hongtao Wang. City Grade Classification Based on Connectivity Analysis by Luojia I Night-Time Light Images in Henan Province, China. Remote Sensing. 2020; 12 (11):1705. Chicago/Turabian Style Zongze Zhao; Gang Cheng; Cheng Wang; Shuangting Wang; Hongtao Wang. 2020. "City Grade Classification Based on Connectivity Analysis by Luojia I Night-Time Light Images in Henan Province, China." Remote Sensing 12, no. 11: 1705. Journal article Water Body Extraction from Very High Spatial Resolution Remote Sensing Data Based on Fully Convolutional Networks Liwei Li Zhi Yan Qian Shen Gang Cheng Lianru Gao Bing Zhang https://doi.org/10.3390/rs11101162 Published: 15 May 2019 in Remote Sensing . Reads? 0 Downloads? 0 Abstract Cite All recommendations This paper studies the use of the Fully Convolutional Networks (FCN) model in the extraction of water bodies from Very High spatial Resolution (VHR) optical images in the case of limited training samples. Two different seasonal GaoFen-2 images with a spatial resolution of 0.8 m in the south of the Beijing metropolitan area were used to extensively validate the FCN model. Four key factors including input features, training data, transfer learning, and data augmentation related to the performance of the FCN model were empirically analyzed by using 36 combinations of various parameter settings. Our findings indicate that the FCN-based method can work as a robust and cost-effective tool in the extraction of water bodies from VHR images. The FCN-based method trained on a small amount of labeled L1A data can also significantly outperform the Normalized Difference Water Index (NDWI) based method, the Support Vector Machine (SVM) based method, and the Sparsity Model (SM) based method, even when radiometric normalization and spatial contexts are introduced to preprocess the input data for the latter three methods. The advantages of the FCN-based method are mainly due to its capability to exploit spatial contexts in the image, especially in urban areas with mixed water and shadows. Though the settings of four key factors significantly affect the performance of the FCN based method, choosing a qualified setting for the FCN model is not difficult. Our lessons learned from the successful use of the FCN model for the extraction of water from VHR images can be extended to extract other land covers. ACS Style Liwei Li; Zhi Yan; Qian Shen; Gang Cheng; Lianru Gao; Bing Zhang. Water Body Extraction from Very High Spatial Resolution Remote Sensing Data Based on Fully Convolutional Networks. Remote Sensing 2019 , 11 , 1162 . AMA Style Liwei Li, Zhi Yan, Qian Shen, Gang Cheng, Lianru Gao, Bing Zhang. Water Body Extraction from Very High Spatial Resolution Remote Sensing Data Based on Fully Convolutional Networks. Remote Sensing. 2019; 11 (10):1162. Chicago/Turabian Style Liwei Li; Zhi Yan; Qian Shen; Gang Cheng; Lianru Gao; Bing Zhang. 2019. "Water Body Extraction from Very High Spatial Resolution Remote Sensing Data Based on Fully Convolutional Networks." Remote Sensing 11, no. 10: 1162. .
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