摘要
大面积农田种植信息的准确获取是精准农业的基础。色彩空间转换、纹理分析和颜色指数等方法能够有效的增强和挖掘影像潜在的信息,对影像分类很有帮助,该文利用2016年9月获取的无人机影像对新疆兵团第八师149团的部分农田进行了作物类型的提取研究。首先对影像进行了色彩空间转换和灰度共生矩阵纹理滤波,得到了27项色彩与纹理特征,通过比较变异系数和差异系数认为亮度、饱和度和红色二阶矩可以作为最优分类特征。其次计算影像的过绿指数(excess green index,EXG)和可见光波段差异植被指数(visible-band difference vegetation index,VDVI),通过阈值对比确定了EXG指数可以有效的区分不同作物类型。最后对比以上2种方法计算得到的分类结果,表明基于色彩与纹理特征提取的作物类型的精度较高,将该方法应用于棉花、玉米和葡萄的分类,误差值分别为7.2%、4.75%和2.37%,明显高于基于颜色指数的提取方法,是一种行之有效的无人机数据作物分类方法。该研究虽未对更大区域做进一步探讨,但可为无人机应用于农田作物分类提供参考。
Xinjiang is the main cultivated area of cotton in China. It is very important to obtain data of planting area every year Access to accurate, large-scale farmland planting information is also the basis for precision agriculture. Many scholars at home and abroad have carried out relevant research. They use different methods to extract information on crop cultivation at different time, but the data are relatively slow to update. Small-scale unmanned aerial vehicle （UAV） remote sensing with low cost, low risk, high temporal and spatial resolution and other characteristics, is very suitable for rapid extraction of crop information and crop classification. Color space conversion, texture analysis and color index and other methods can effectively enhance and tap the potential information of the image, which is helpful to the image classification. In this paper, we used the UAV images acquired in September 2016 to carry out the extraction of crop types in some farmland of the Eighth Division of Xinjiang Corps. Through the conversion of color space and the processing of different texture filtering, the texture features of the objects in the image could be solved satisfactorily, which could better solve the phenomenon of the same spectrum and the heterogeneity of the same kind, and improve the recognition accuracy of the feature. First, the color space conversion and the gray level co-occurrence texture filtering were carried out, and 27 color and texture features were obtained. By comparing the coefficient of variation and the difference coefficients of the 3 color features and 24 texture features, we believed that the brightness, saturation and red second order moment could be used as the optimal classification characteristics. Secondly, due to the lack of near-infrared band data, only the visible light red band and green band were used to build color index to extract vegetation information. This paper calculated the excess green index （EXG） and the visible-band difference vegetation index （VDVI） of the image. By comparing the threshold of the EXG and the VDVI of the gray scale image, it was determined that the EXG could effectively distinguish the different crop types. Finally, the visual interpretation results were compared with the results based on the combination of color texture feature classification and color index classification. The results showed that the measured areas of cotton, maize and grape were 0.490 1, 0.042 1 and 0.143 2 km2, respectively. The areas of the 3 crop types based on color and texture features were 0.454 8, 0.044 1, and 0.139 8 km2, respectively, and the areas of the 3 crop types based on the color index feature were 0.547 7, 0.039 8 and 0.099 4 km2, respectively. The error values for the former method when applied to the classification of cotton, maize and grape were 7.2%, 4.75% and 2.37%, respectively. The results showed that the extraction accuracy of crop type based on color and texture feature was higher than that of color index. However, both of the methods are based on single pixel. For the same kind of crop, due to differences in spectral characteristics, some internal area is included into other crop types, and there is a significant salt and pepper effect. Related data post-processing for the wrongly classified small patch should be performed to improve the classification accuracy. In some researches, the object-oriented classification method on the basis of calculating the correlation index can better solve the problem of discontinuities and incompleteness of the same object based on the pixel classification method. At the same time, the study of farmland information extraction of larger scale UAV data cannot be carried out in this paper, which will be further explored in the follow-up study.
作者
郭鹏
武法东
戴建国
王海江
徐丽萍
张国顺
Guo Peng Wu Fadong Dai Jianguo Wang Haijiang Xu Liping Zhang Guoshun(College of Earth Sciences and Resources, China University of Geoscience, Beo＇ing 100083, China College of Science, Shihezi University, Shihezi 832003, China College of Information Science ＆ Technology, Shihezi University, Shihezi 832003, China College of Agriculture, Shihezi University, Shihezi 832003, China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2017年第13期112-119,共8页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金项目(31460317)
国家国际科技合作专项项目(2015DFA11660)
关键词
无人机
遥感
农作物
色彩与纹理特征
颜色指数
农田
分类
unmanned aerial vehicle
remotesensing
crops
color and texture features
color index
farmland
classification
