吴健生, 刘建政, 黄秀兰, 彭 建, 李慧坚. 基于面向对象分类的土地整理区农田灌排系统自动化识别[J]. 农业工程学报, 2012, 28(8): 25-31.
    引用本文: 吴健生, 刘建政, 黄秀兰, 彭 建, 李慧坚. 基于面向对象分类的土地整理区农田灌排系统自动化识别[J]. 农业工程学报, 2012, 28(8): 25-31.
    Wu Jiansheng, Liu Jianzheng, Huang Xiulan, Peng Jian, Li Huijian. Automatic identification of irrigation and drainage system in land reclamation area based on object-oriented classification[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2012, 28(8): 25-31.
    Citation: Wu Jiansheng, Liu Jianzheng, Huang Xiulan, Peng Jian, Li Huijian. Automatic identification of irrigation and drainage system in land reclamation area based on object-oriented classification[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2012, 28(8): 25-31.

    基于面向对象分类的土地整理区农田灌排系统自动化识别

    Automatic identification of irrigation and drainage system in land reclamation area based on object-oriented classification

    • 摘要: 针对目前农田灌排系统识别研究中影像分辨率低、自动化程度不高的问题,该研究基于面向对象分类法,提出了一套从影像到农田灌排系统矢量输出的完整提取流程。研究利用KOMPSAT-2高分辨率遥感影像数据,以吉林省西部土地整理大安项目区作为试验区,使用自主研发的识别程序对土地整理区农田灌排系统进行了自动化识别提取,并与监督分类法、手动屏幕数字化作了对比分析。结果显示,基于面向对象分类的自动化提取方法在精度上与手动屏幕数字化相近,总体精度达到了89.64%,远高于监督分类法的识别精度;而且该方法所耗费的时间最少,操作过程不需人工干预,识别结果的稳定性也高于另外两种方法。研究表明,基于面向对象分类的自动化提取方法,是一种较理想的土地整理区农田灌排系统遥感监测手段,同时也为其他地物监测提供了一种有效的途径。

       

      Abstract: To identify irrigation and drainage system in land reclamation area automatically, an object-oriented classification method was proposed. The effectiveness of this method was compared with supervised classification method and manual screen digitization in terms of recognition accuracy and efficiency. KOMPSAT-2 high-resolution remote sensing images were selected as the experimental data, and the study area is located in Da’an city of western Jilin province. The experimental results showed that the overall recognition accuracy of object-oriented classification method was 89.64%, much higher than the accuracy of supervised classification method. More over, the object-oriented classification method is less time-consuming than manual screen digitization. The object-oriented classification method needs the least human intervention to complete the classification process and could achieve more stable results than the other two methods. Results show that the object-oriented classification is a powerful tool for remote sensing monitoring of irrigation and drainage system in land reclamation area. Meanwhile, this research provides an effective way for the identification of other ground objects in land reclamation projects.

       

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