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spatial relationship-assisted classification from high-resolution remote sensing imagery

International Journal of Digital Earth 2015年第9期8卷 710-726页

作者： Cheng Qiao Jinfei Wang Jiali Shang Bahram Daneshfar Department of Geography The University of Western OntarioLondonONCanada Science&Technology Branch Agriculture and Agri-Food CanadaOttawaONCanada

spatial information remains to be an important topic in geographic information system and in remote sensing fields,and spatial relationships have been increasingly incorporated into the image classification processes.Previous studies have employed multiple occurrences of spatial features(shape,texture,etc.,)to improve classification results.However,less attention has been focused on using higher-level spatial relationships for image classification.In this study,two novel spatial relationships,namely,maximum spatial adjacency(MSA)and directional spatial adjacency(DSA),were proposed to assist in image classification.The proposed methods were implemented to extract buildings,beach,and emergent vegetation land-cover classes according to their spatial relationships with their corresponding reference classes.The promising results obtained from this study suggest that the proposed MSA and DSA spatial relationships can be valuable information in defining rule sets for a more reasonable and accurate classification.

关键词： spatial relationship image classification high-resolution imagery maximum spatial adjacency directional spatial adjacency

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Optimizing spatial relationships in GCN to Improve the Classification Accuracy of Remote Sensing Images

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Intelligent Automation & Soft Computing 2023年第7期37卷 491-506页

作者： Zimeng Yang Qiulan Wu Feng Zhang Xuefei Chen Weiqiang Wang XueShen Zhang School of Information Science&Engineering Shandong Agricultural UniversityTaian271018China

Semantic segmentation of remote sensing images is one of the core tasks of remote sensing image interpretation.With the continuous develop-ment of artificial intelligence technology,the use of deep learning methods for interpreting remote-sensing images has matured.Existing neural networks disregard the spatial relationship between two targets in remote sensing images.Semantic segmentation models that combine convolutional neural networks(CNNs)and graph convolutional neural networks(GCNs)cause a lack of feature boundaries,which leads to the unsatisfactory segmentation of various target feature boundaries.In this paper,we propose a new semantic segmentation model for remote sensing images(called DGCN hereinafter),which combines deep semantic segmentation networks(DSSN)and GCNs.In the GCN module,a loss function for boundary information is employed to optimize the learning of spatial relationship features between the target features and their relationships.A hierarchical fusion method is utilized for feature fusion and classification to optimize the spatial relationship informa-tion in the original feature information.Extensive experiments on ISPRS 2D and DeepGlobe semantic segmentation datasets show that compared with the existing semantic segmentation models of remote sensing images,the DGCN significantly optimizes the segmentation effect of feature boundaries,effectively reduces the noise in the segmentation results and improves the segmentation accuracy,which demonstrates the advancements of our model.

关键词： Remote sensing image semantic segmentation GCN spatial relationship feature fusion

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Image Retrieval Based on Objects’ spatial Orientation relationships

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Wuhan University Journal of Natural Sciences 2005年第3期10卷 520-524页

作者： CHENChuan-bo LIQi-shen GONGWen-jie CollegeofComputerScienceandTechnology HuazhongUniversityofScienceandTechnologyWuhan430074HubeiChina

A new concept of characteristic scanning radial (CSR) is proposed for thesegmented image on the basis of two shape-specific points of its shape-objects. Subsequently, twocharacteristic attribute sequences (CAS) of relative distance and relative direction are derived torepresent the spatial orientation relationships among objects of the image. A novel image retrievalalgorithm is presented using these two CASs. The proposed retrieval approach not only satisfies thetransformational invariance, butalso attains the quantitative comparison of matching. Experimentsidentify the effectiveness and efficiency of the algorithm adequately.

关键词： image retrieval spatial relationship transformation invariant shape-specific point characteristic scanning radial characteristic attribute sequence

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Comparison between CBR and CA methods for estimating land use change in Dongguan, China

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Journal of Geographical Sciences 2012年第4期22卷 716-736页

作者： DU Yunyan GE Yong V.Chris LAKHAN SUN Yeran CAO Feng State Key Laboratory of Resources and Environmental Information System Institute of Geographic Science and Natural Resources ResearchCASBeijing 100101China Department of Earth and Environmental Sciences University of WindsorON N9B 3P4Canada

Many studies on land use change （LUC）, using different approaches and models, have yielded good results. Applications of these methods have revealed both advantages and limitations. However, LUC is a complex problem due to influences of many factors, and variations in policy and natural conditions. Hence, the characteristics and regional suitability of different methods require further research, and comparison of typical approaches is required. Since the late 1980s, CA has been used to simulate urban growth, urban sprawl and land use evolution successfully. Nowadays it is very popular in resolving the LUC estimating problem. Casebased reasoning （CBR）, as an artificial intelligence technology, has also been employed to study LUC by some researchers since the 2000s. More and more researchers used the CBR method in the study of LUC. The CA approach is a mathematical system constructed from many typical simple components, which together are capable of simulating complex behavior, while CBR is a problem-oriented analysis method to solve geographic problems, particularly when the driving mechanisms of geographic processes are not yet understood fully. These two methods were completely different in the LUC research. Thus, in this paper, based on the enhanced CBR model, which is proposed in our previous research （Duet aL 2009）, a comparison between the CBR and CA approaches to assessing LUC is presented. LUC in Dongguan coastal region, China is investigated. Applications of the improved CBR and the cellular automata （CA） to the study area, produce results demonstrating a similarity estimation accuracy of 89% from the improved CBR, and 70.7% accuracy from the CA. From the results, we can see that the accuracies of the CA and CBR approaches are both 〉70%. Although CA method has the distinct advantage in predicting the urban type, CBR method has the obvious tendency in predicting non-urban type. Considering the entire analytical process, the preprocessing workload in CBR is less than that of the CA approach. As such, it cou

关键词： artificial intelligence case-based reasoning land use changes spatial relationship cellular automata Dongguan coastal region, China

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