赵庆展,刘伟,尹小君,张天毅.基于无人机多光谱影像特征的最佳波段组合研究[J].农业机械学报,2016,47(3):242-248.
Zhao Qingzhan,Liu Wei,Yin Xiaojun,Zhang Tianyi.Selection of Optimum Bands Combination Based on Multispectral Images of UAV[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(3):242-248.
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基于无人机多光谱影像特征的最佳波段组合研究   [下载全文]
Selection of Optimum Bands Combination Based on Multispectral Images of UAV   [Download Pdf][in English]
投稿时间:2015-12-05  
DOI:10.6041/j.issn.1000-1298.2016.03.034
中文关键词:  无人机  多光谱传感器  最佳波段组合  光谱特征  纹理特征
基金项目:国家自然科学基金项目(31260291)和新疆生产建设兵团科技计划项目(2015BA006)
作者单位
赵庆展 石河子大学
兵团空间信息工程技术研究中心 
刘伟 石河子大学
兵团空间信息工程技术研究中心 
尹小君 石河子大学
兵团空间信息工程技术研究中心 
张天毅 石河子大学
兵团空间信息工程技术研究中心 
中文摘要:针对卫星遥感影像分辨率低、时间周期长、波段冗余信息多等问题,利用无人机多光谱数据获取便捷、成本低、周期短的优势,以玛纳斯河畔为研究区,使用固定翼无人机搭载Micro MCA12 Snap多光谱传感器获取高分辨率多光谱影像。通过对多光谱影像数据标准差及相关性进行分析排序,结合OIF方法得到原始波段最佳组合,使用多种植被及水体指数、主成分分析、灰度共生矩阵确定信息量最大的光谱特征与纹理特征波段,提出将光谱特征、纹理特征信息与最佳波段指数结合的方法来确定地物分类最佳波段组合。实验结果表明,针对Micro MCA12 Snap多光谱传感器,可选择波段1、6、11、NDVI、NDWI以及灰度共生矩阵中的Mean参量作为其地物分类的最佳波段组合。感兴趣区域内非监督IsoData分类精度从83.57%提升到89.80%,监督的SVM分类精度从95.58%提升到99.76%。研究结果可为无人机多光谱遥感最佳波段组合选择提供借鉴和参考。
Zhao Qingzhan  Liu Wei  Yin Xiaojun  Zhang Tianyi
Shihezi University;Geospatial Information Engineering Research Center, Xinjiang Production and Construction Corps,Shihezi University;Geospatial Information Engineering Research Center, Xinjiang Production and Construction Corps,Shihezi University;Geospatial Information Engineering Research Center, Xinjiang Production and Construction Corps and Shihezi University;Geospatial Information Engineering Research Center, Xinjiang Production and Construction Corps
Key Words:unmanned aerial vehicle  multi spectral sensors  optimum bands combination  spectral feature  texture feature
Abstract:With the rapid development of unmanned aerial vehicle(UAV), it is widely used in the field remote sensing which is different from the satellite remote sensing, and has many advantages such as more convenient, lower cost, and shorter revisit cycle. However, effective information cannot be extracted from the multispectral images of UAV easily because of the high resolution multi band redundant data which can increase the complexity of data processing and consume a lot of computational resources. Therefore, the purpose of this research is to study the optimum bands combination which can be extracted by multispectral image. Manas’s riverside in Shihezi, Xinjiang was selected as research area. Fixed wing UAV equipped with Micro MCA12 Snap was used to obtain high resolution multispectral images. Based on this system, a method was proposed to select the optimum bands combination for topographical objects classification. First, the standard deviation and correlation coefficient of the multi spectral image’s gray value were analyzed; the original bands combinations were got with the OIF method. Then, the most informative spectral feature bands and texture feature bands were determined respectively by using variety methods, such as vegetation and water index, principal component analysis, and GLCM. Finally, the original bands combination, spectral feature bands and texture feature bands were combined to obtain the final result. According to the analysis, bands 1,6,11, NDVI, NDWI and the mean parameter of GLCM combination of Micro MCA12 Snap multi spectral sensors were selected as the optimum bands combination for topographical objects classification. After the selection of the bands combination, unsupervised classification and supervised classification methods were used to verify the classification accuracy with the optimum bands combination respectively. The classification accuracy with IsoData of ROI (region of interest) was increased from 83.57% to 89.80%, when it comes to SVM, the accuracy was increased from 95.58% to 99.76%. In addition, the study also provides effective reference for the selection of optimum bands combination with UAV multispectral images.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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