| 基于Landsat 8 OLI辅助的亚米级遥感影像树种识别 |
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| 引用本文： | 魏晶昱,毛学刚,方本煜,包晓建,许振宇.基于Landsat 8 OLI辅助的亚米级遥感影像树种识别[J].北京林业大学学报,2016,38(11):23-33. |
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| 作者姓名： | 魏晶昱 毛学刚 方本煜 包晓建 许振宇 |
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| 作者单位： | 东北林业大学林学院 |
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| 基金项目： | 国家自然科学基金项目(31300533)，“十二五”国家科技支撑计划项目(2012AA102001)，东北林业大学大学生创新项目(201410225152)。 |
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| 摘 要： | 为研究高空间分辨率遥感影像与多光谱遥感影像协同进行面向对象树种识别的有效性,本研究以QuickBird高空间分辨率(全色0.61 m)和Landsat 8 OLI(30 m)遥感影像为基础数据,在分类的过程中采用2种分割方案(有Landsat 8 OLI遥感影像辅助的QuickBird遥感影像分割和无Landsat 8 OLI遥感影像辅助的QuickBird遥感影像分割)进行多尺度分割,对2种分割方案进行比较。基于QuickBird遥感影像和Landsat 8 OLI遥感影像提取光谱、纹理、空间3方面68种分类特征,应用最邻近分类法和支持向量机分类方法进行面向对象树种分类,采用相同的分类系统、统一的分割尺度以及同一套验证样本,利用Kappa系数、总精度、生产者精度和用户精度4个评价指标进行精度评价。结果表明:单独使用QuickBird高空间分辨率遥感影像的分割结果优于QuickBird高空间分辨率遥感影像与Landsat 8 OLI遥感影像协同分割的结果,最优分割阈值与合并阈值分别为25和90。在最优分割结果的基础上,多光谱Landsat 8 OLI遥感影像与QuickBird高空间分辨率遥感影像协同进行面向对象分类,最邻近分类法和支持向量机分类方法的分类总精度分别为85.35%(Kappa=0.701 3)和88.12%(Kappa=0.853 6);单独使用QuickBird高空间分辨率遥感影像进行面向对象分类,2种方法的分类总精度分别为79.67%(Kappa=0.693 9)和83.33%(Kappa=0.792 5)。QuickBird遥感影像在Landsat 8 OLI遥感影像辅助下,分类结果的地物边界更加清晰,总体精度及主要树种识别精度均得到了提高。研究成果应用在实地森林调查与区划时可有效缩短调査时间、减少调查成本、降低劳动强度、提高成果质量。
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| 关 键 词： | 面向对象 QuickBird 森林类型 高空间分辨率 |
| 收稿时间： | 2016-02-23 |
Submeter remote sensing image recognition of trees based on Landsat 8 OLI support |
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| WEI Jing-yu,MAO Xue-gang,FANG Ben-yu,BAO Xiao-jian,XU Zhen-yu.Submeter remote sensing image recognition of trees based on Landsat 8 OLI support[J].Journal of Beijing Forestry University,2016,38(11):23-33. |
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| Authors: | WEI Jing-yu MAO Xue-gang FANG Ben-yu BAO Xiao-jian XU Zhen-yu |
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| Institution: | School of Forestry, Northeast Forestry University, Harbin, Heilongjiang, 150040, P. R. China. |
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| Abstract: | In order to study the validity of the object-based identification of tree species with high spatial resolution remote sensing image ( QuickBird) and multi spectral remote sensing image ( Landsat 8 OLI) coordinated, based on QuickBird high spatial resolution ( panchromatic 0. 61 m) remote sensing image and Landsat 8 OLI(30 m) remote sensing image, we used 2 segmentation schemes ( segmentation based on QuickBird remote sensing image with Landsat 8 OLI remote sensing image as an auxiliary or not) to do multi-scale segmentation, and compared the 2 segmentation schemes in the classification processing. This research applied nearest neighbor classification and support vector machine object-based classification methods, the same classification system, the unified segmentation scale and the same set of validation samples to classify tree species with 68 classification features in terms of spectral, texture and spatial extracted by QuickBird remote sensing image and Landsat 8 OLI remote sensing image, and then take use of Kappa coefficient, total accuracy, producer accuracy and user accuracy to evaluate the accuracy. The results showed that the segmentation result based on QuickBird high spatial resolution remote sensing image only was better than that based on QuickBird high spatial resolution remote sensing image and Landsat 8 OLI remote sensing image coordinated. The best segmentation threshold was 25 and the best merging threshold was 90. On the basis of the best segmentation threshold, applying Landsat 8 OLI multi spectral remote sensing image and QuickBird high spatial resolution remote sensing image together to take the object-based classification, the total accuracy of nearest neighbor classification method and support vector machine classification method was 85. 35% ( Kappa = 0. 701 3 ) and 88. 12% ( Kappa=0. 853 6 ) . And the total accuracy of the above two methods was 79. 67% ( Kappa =0. 693 9 ) and 83. 33% ( Kappa =0. 792 5 ) when using the QuickBird high spatial resolution remote sensing image only. Under the support of Landsat 8 OLI remote sensing image, object boundary of the classification result is clearer, the total accuracy and the accuracy of major tree species are significantly improved. The research results can effectively shorten the time and reduce the cost of investigation and survey, reduce labor intensity, improve the quality of products when it was applied in field forest survey and zoning. |
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| Keywords: | object-based method QuickBird forest classification high spatial resolution |
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