期刊：
IEEE Transactions on Intelligent Transportation Systems,2021年22(2):1276-1286 ISSN：1524-9050
通讯作者：
Sui, Haigang
作者机构：
[Zhou, Mingting; Feng, Wenqing; Chen, Xu; Sui, Haigang] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China.;[Fang, Jian] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Sui, Haigang] W;Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China.
关键词：
Spatiotemporal phenomena;Visualization;Cameras;Feature extraction;Interference;Three-dimensional displays;Tensors;Distance-based global and partial multi-regional network;global similarity;distance-based classification;vehicle re-identification
摘要：
Vehicle re-identification supports cross-camera tracking and the location of specific vehicles in a smart city. The gallery images of vehicles are ranked based on the similarities in the appearance of objects to a vehicle query image. Previous work on vehicle re-identification has mainly focused on global or local analyses of predefined regions of vehicles to classify the vehicle images with a softmax loss function. On the one hand, separate global or predefined local regions of vehicles are often sensitive to perspective and occlusions. On the other hand, the embedding space supervised by the softmax loss function is not sufficiently compact for the object class. To solve these problems, we propose an end-to-end distance-based global and partial multi-regional deep network (DGPM) that combines multi-regional features to identify global and local differences. We exploit a three-branch architecture to learn the global and partial features from coarsely partitioned regions. A global similarity module is introduced to reduce the background information interference in the local branches. Unlike general classification, we design a distance-based classification layer that maintains consistency among criteria for similarity evaluation. Furthermore, we use spatiotemporal vehicle information to improve the vehicle re-identification results when the camera and shooting time are available. Systematic comparative evaluations performed on the large-scale VeRi and VehicleID datasets showed that our approach robustly achieved state-of-the-art performance. For instance, for the VeRi dataset, we achieve (79.39 + 2.78)% mAP and (96.19 + 2.26)% Rank-1 accuracy.
摘要：
Land use reflects human activities on land. Urban land use is the highest level human alteration on Earth, and it is rapidly changing due to population increase and urbanization. Urban areas have widespread effects on local hydrology, climate, biodiversity, and food production [1,2]. However, maps, that contain knowledge on the distribution, pattern and composition of various land use types in urban areas, are limited to city level. The mapping standard on data sources, methods, land use classification schemes varies from city to city, due to differences in financial input and skills of mapping personnel. To address various national and global environmental challenges caused by urbanization, it is important to have urban land uses at the national and global scales that are derived from the same or consistent data sources with the same or compatible classification systems and mapping methods. This is because, only with urban land use maps produced with similar criteria, consistent environmental policies can be made, and action efforts can be compared and assessed for large scale environmental administration. However, despite of the fact that a number of urban-extent maps exist at global scales [3,4], more detailed urban land use maps do not exist at the same scale. Even at big country or regional levels such as for the United States, China and European Union, consistent land use mapping efforts are rare [5,6](e.g., https://sdi4apps.eu/open_land_use/).
作者：
Wu, Hao;Li, Zhen;Clarke, Keith C.;Shi, Wenzhong;Fang, Linchuan*;...
期刊：
International Journal of Geographical Information Science,2019年33(5):1040-1061 ISSN：1365-8816
通讯作者：
Fang, Linchuan
作者机构：
[Zhou, Jie; Wu, Hao; Lin, Anqi] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan, Hubei, Peoples R China.;[Zhou, Jie; Wu, Hao; Lin, Anqi] Cent China Normal Univ, Hubei Prov Key Lab Geog Proc Anal & Simulat, Wuhan, Hubei, Peoples R China.;[Li, Zhen] Wuhan Univ Technol, Sch Resources & Environm Engn, Wuhan, Hubei, Peoples R China.;[Clarke, Keith C.] Univ Calif Santa Barbara, Dept Geog, Santa Barbara, CA 93106 USA.;[Shi, Wenzhong] Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Kowloon, Hong Kong, Peoples R China.
通讯机构：
[Fang, Linchuan] N;Northwest A&F Univ, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling, Shaanxi, Peoples R China.
关键词：
Land use change simulation;Markov chain;cellular automata;response surface method;spatial scale sensitivity
摘要：
Understanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size.
