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Remote Sensing Technology and Application
Classification of Remote Sensing Image based on Object-oriented Method:A Case Study of Baixiang County
Jiang Dong1,Chen Shuai1,2,Ding Fangyu1,2,Fu Jingying1,2,Hao Mengmeng1,2
(1.Key Laboratory of Resource Utilization and Environmental Remediation,Institute of GeographicSciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;2.College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 100049,China)
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Abstract  Remote sensing is the main means of extracting land cover types,which has important significance for monitoring land use change and developing national policies.Object-based classification methods can provide higher accuracy data than pixel-based methods by using spectral,shape and texture information.In this study,we choose GF-1 satellite’s imagery and proposed a method which can automatically calculate the optimal segmentation scale.The object-based methods for classifying four typical land cover types are compared using multi-scale segmentation and three supervised machine learning algorithms.The relationship between the accuracy of classification results and the training sample proportion is analyzed and the result shows that object-based methods can achieve higher classification results in the case of small training sample ratio,overall accuracies are higher than 94%.Overall,the classification accuracy of support vector machine is higher than that of neural network and decision tree during the process of object-oriented classification.
Key words:  Object-based classification      GF-1      Optimal scale      Machine learning     
Received:  19 January 2017      Published:  16 March 2018
TP 79  
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Jiang Dong
Chen Shuai
Ding Fangyu
Fu Jingying
Hao Mengmeng

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Jiang Dong,Chen Shuai,Ding Fangyu,Fu Jingying,Hao Mengmeng. Classification of Remote Sensing Image based on Object-oriented Method:A Case Study of Baixiang County. Remote Sensing Technology and Application, 2018, 33(1): 143-150.

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