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《Remote Sensing Technology and Application》 2018-01
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Classification of Remote Sensing Image based on Object-oriented Method:A Case Study of Baixiang County

Jiang Dong;Chen Shuai;Ding Fangyu;Fu Jingying;Hao Mengmeng;Key Laboratory of Resource Utilization and Environmental Remediation,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences;College of Resources and Environment,University of Chinese Academy of Sciences;  
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.
【Fund】: 国家自然科学基金项目(41571509);; 2015年度环保公益性行业科研专项项目(201509044)
【CateGory Index】: TP751
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