| 高分六号遥感卫星新增波段下的树种分类精度分析 |
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| 引用本文： | 张沁雨,李哲,夏朝宗,陈健,彭道黎.高分六号遥感卫星新增波段下的树种分类精度分析[J].地球信息科学,2019,21(10):1619-1628. |
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| 作者姓名： | 张沁雨 李哲 夏朝宗 陈健 彭道黎 |
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| 作者单位： | 1. 北京林业大学林学院,北京 100083;2. 国家林业和草原局调查规划设计院,北京 100714 |
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| 基金项目： | 国家林业局948项目(2015-4-32);高分林业遥感应用示范系统（二期）项目 |
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| 摘 要： | 高分六号卫星具有覆盖广、多种分辨率、波段多的优势,能为遥感解译提供更丰富的信息。为探究高分六号卫星新增波段在森林树种识别上的应用,本文以覆盖根河市阿龙山林业局的一期高分六号宽幅影像为数据源,基于特征优化空间算法（Feature Space Optimization,FSO）和最大似然分类法,分别利用高分六号的前4个波段和所有波段（8波段）的光谱、纹理等特征进行了森林树种分类,并逐一添加新增波段特征确定了各波段的贡献率排名。结果表明：在加入了优选出的均匀性纹理、均值纹理和角二阶矩纹理3种纹理特征后,前4波段和8波段的分类精度比只基于光谱特征时的精度分别高出13.23%和24.63%;利用8波段信息比只利用前4波段在基于光谱特征上的精度高11.88%,在基于光谱+纹理特征上则高23.24%;基于8波段光谱+纹理特征的树种分类精度最高,达到68.74%,新增4波段的贡献率排名为B6>B5>B8>B7,说明新增红边波段对于本次树种分类试验的贡献率最高,能为北方树种识别提供有效帮助。
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| 关 键 词： | 高分六号遥感卫星 树种分类 特征优选 纹理特征 红边波段 新增波段 根河市阿龙山林业局 |
| 收稿时间： | 2019-03-13 |
Tree Species Classification based on the New Bands of GF-6 Remote Sensing Satellite |
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| ZHANG Qinyu,LI Zhe,XIA Chaozong,CHEN Jian,PENG Daoli.Tree Species Classification based on the New Bands of GF-6 Remote Sensing Satellite[J].Geo-information Science,2019,21(10):1619-1628. |
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| Authors: | ZHANG Qinyu LI Zhe XIA Chaozong CHEN Jian PENG Daoli |
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| Institution: | 1. College of Forestry, Beijing Forestry University, Beijing 100083, China;2. Academy of Forest Inventory and Planning, National Forestry and Grassland Administration, Beijing 100714, China |
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| Abstract: | GaoFen-6 satellite (GF-6), with the advantages of wide coverage, various resolutions, and multiple bands, provides valuable information to interpret remote sensing data. The objective of this study is to explore the application of the new bands of GF-6 in the ide.pngication of forest tree species. GF-6 WFV image data that covers the whole Alongshan Forestry Bureau was used as the material. Based on the feature space optimization (FSO) algorithm and maximum likelihood classification method, the spectral features and Gray-Level Co-occurrence Matrix (GLCM) features were applied to the first 4 bands and all bands (8 bands) of GF-6 WFV to classify main forest trees (including Larix gmelinii (Rupr.) Kuzen., Betula platyphylla Suk, Pinus sylvestris var mongolica Litv, Salix and Populus davidiana) by utilizing the bands texture characteristics. Furthermore, to study the amount of information provided by the new bands in the classification of tree species, several new band features were added individually to the whole classification features to determine the contribution rate rank of each band. To ensure that the classification of the first 4 bands and 8 bands is comparable, the spectral features and three texture features of each bands were used for experimental classification of tree species. Results show that, for the 34 features of 4 bands and 68 features of 8 bands, the preferred texture features were mainly the GLCM Homogeneity, the GLCM Mean, and GLCM Angular second moment. The classification accuracy after adding the preferred texture features increased 13.23% and 24.63% than only using 4 bands and 8 bands, respectively. Regarding the use of different bands for classification, the classification accuracy of using 8 bands was 11.88% higher than the using first 4 bands when based on spectral features while 23.24% higher when both spectral and texture features were applied. The tree species based on 8 bands spectral and texture features has the highest classification accuracy of 68.74%. These findings suggest that the new bands of GF-6 WFV could improve the accuracy of tree species classification. Moreover, the contribution rates of the new bands rank as follows: Band 6 (Red Edge 2)> Band 5 (Red Edge 1)> Band 8 (Yellow)> Band 7 (Purple). This indicates that red edge band contributes most to tree species classification in northern China. |
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| Keywords: | GF-6 remote sensing satellite tree species classification feature optimization texture feature red edge band new bands Alongshan Forestry Bureau Genhe City |
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