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当前位置: 首页 > 文章 > 基于色彩运算和混沌粒子群滤波的土壤粗糙度测算 农业机械学报 2015,46 (3) 158-165
Position: Home > Articles > Soil Surface Roughness Measurement Based on Color Operation and Chaotic Particle Swarm Filtering Transactions of the Chinese Society for Agricultural Machinery 2015,46 (3) 158-165

基于色彩运算和混沌粒子群滤波的土壤粗糙度测算

作  者：
李俐;王荻;王鹏新;黄健熙;朱德海
单  位：
中国农业大学信息与电气工程学院
关键词：
微波遥感;土壤粗糙度;测算;色彩操作;混沌粒子群优化
摘  要：
采用插入参考刻度板读取土壤高度信息是一种简单易行的土壤粗糙度测量方法。针对人工读取效率低、基于图像的计算机读取精度受农田环境光照的影响和土壤表面异物(如杂草、植被)影响的问题,提出了一种简化参考刻度板的图像获取和全自动图像处理的粗糙度测算方法,该方法在利用色彩运算和阈值分割消除杂草和阴影影响的基础上,进行土壤边界提取和比例尺的计算,进而获得土壤粗糙度。为了提高方法的自动化程度和鲁棒性,阈值分割采用混沌粒子群优化滤波实现。实验表明,结合色彩运算和混沌粒子群优化的土壤粗糙度测算方法降低了对拍摄环境的要求,能快速高效地计算土壤粗糙度,所提取的土壤轮廓线高度误差控制在0.5 cm以下,所得均方根高度误差在5%以内,相关长度的计算误差在1%以内,满足了土壤粗糙度实时在线测算的要求。
译  名：
Soil Surface Roughness Measurement Based on Color Operation and Chaotic Particle Swarm Filtering
作  者：
Li Li;Wang Di;Wang Pengxin;Huang Jianxi;Zhu Dehai;College of Information and Electrical Engineering,China Agricultural University;
关键词：
Microwave remote sensing Soil surface roughness Measurement Color operation;;Chaotic particle swarm optimization
摘  要：
It is easy to measure soil surface roughness by using a reference white board with a scale,from which the interface between soil and reference board can be detected and information of soil height can be read. Considering the low efficiency of manual reading,compute reading will be a good choice. But the impact of field illumination and weed interference makes compute reading susceptible. A soil roughness measuring method was proposed. In the proposed method,images were acquired with simplified reference scaling board. To process image automatically,color operation and threshold segmentation were used to decrease the effects of weeds and shadow,and then the soil boundary and scale were acquired,which would be used to measure the soil roughness. To improve the automaticity and robustness of the measuring method,chaotic particle swarm filter was applied for threshold segmentation. The test results showed that the soil roughness measurement method using color operation and chaotic particle swarm optimization reduced the requirement to image acquirement environment,and could calculate soil roughness quickly and efficiently,with the height error less than 0. 5 cm,the root mean square height error less than 5%,and the correlation length error less than 1%,which met the requirements of soil roughness real-time on site measurement.

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