基于无人机采集图像的土壤湿度预测模型研究
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摘要
土壤湿度是气候、水文、生态和农业等方面研究的一个重要的基础参数,它直接控制着陆面和大气之间水、热量的输送和平衡。土壤湿度的变化会引起土壤热学特性、地表光学特性的改变,从而影响气候的变化。区域性和大尺度的陆地土壤湿度变化信息对于陆气交互作用平衡和陆面水文研究、改善区域及全球气候模式预报结果、水涝和干早的监测、农作物生长态势评估、自然和生态环境问题的研究等都是十分关键的因素。因而,研究区域或大范围的土壤水分有着特别重要的意义,这也是目前国际上研究的一个重要课题之一。
传统的地面观测站网络不能满足大尺度土壤水分的时间、空间连续动态变化研究的需要。而现在广泛应用的微波测量方式虽然穿透力强,也存在设备过于沉重、短波范围内电测波信号受大气干扰比较大、波段难以控制等缺点,限制了其在农业自动化方面的发展。目前也有很多用可见光-近红外-热红外等光学遥感手段来获取土壤湿度时空分布信息,采用光学遥感具有体积小、成像简单、周期短、费用低等优点,利于将来的农业普及。但是这种手段在目前的应用中大多依靠高空飞行器负载,因为光学遥感的波段无法穿透云层,所以在实际应用中受到了限制。而无人机的出现解决了这些问题。
无人机作为一种新型的信息获取方式,伴随着技术的逐渐成熟,已经在越来越多的领域得到了应用。与通常信息获取使用的飞行器以及卫星相比,无人机具有费用低、精确度高、运行周期短、易于操作等优点,非常适合将来在农业当中普及。本研究正是基于此,选用无人机作为飞行器,可见光-近红外光作为遥感测量的手段,以美国犹他州为例研究如何探测一定区域内土壤表层的湿度信息的问题。本研究的主要内容如下:
首先,建立了图像的多空间分析模型以及分数阶植被归一化公式。用无人机采集回的可见光以及近红外光等信息都是独立的,与土壤湿度的信息的关联都不足以达到预测的标准。以往的研究都是通过各种分析公式,比如HIS、GRAY、NDVI等,将探测到的信息融合起来,达到了较为准确预测土壤湿度的标准。而本研究通过对比图像的各种空间分析特性,提出了分数阶的植被归一化公式,能够大大提高土壤湿度与图像包含信息的相关系数,保证利用建立的模型预测土壤湿度的准确性。
其次,提出了根据无人机图像特点的的图像拼接算法。很多已有的拼接算法已经能够很好的完成图像的镶嵌任务。但是,这些算法运行需要较多的时间,达不到无人机图像拼接的实时要求。出于这个目的,本研究提出了基于局部灰度匹配的图像拼接算法,在图像拼接的过程中融合无人机的飞行特征,大大缩小了图像匹配特征点的搜索范围。并且图像灰度为快速转换的图像特性,在此条件下特征点能够准确、快速匹配,节约了运行成本,达到了无人机图像实时拼接的要求。
第三,完成了无人机图像几何校正算法。由于无人机在飞行的过程受到航线、风速等不确定的影响,所拍摄的图像与地面会产生不确定的夹角。本研究提出了无人机图像几何校正算法,能够根据无人机即时记录下的信息,自动完成图像角度的校正,为图像拼接打下基础。
第四,提出了基于聚类分析算法的图像分割模型。图像中确定区域的边界对土壤湿度的描述以及农业自动灌溉信息的提取至关重要。通常的分割算法都是基于着色点的分布概率或者图像的特征空间分析进行像素点归类,结果比较粗糙。本研究通过聚类分析算法,根据边界点的梯度以及分布趋势,划分区域边界,效果较好。
最后,通过实验验证了之前提出的算法,并且对预测模型的精度进行了分析。结果表明,该模型达到了预测的要求,可以在实际中应用。
Soil moisture is one of essential Parameters in the study of climatology,hydrology,ecology and agriculture,which dominates the transportation and balance of water and heat between lands and air directly. The change of the soil moisture will lead to change of the thermotics characteristic of soil and optical properties of surface,even to the change of climate. The information of the change of land soil moisture in regional and large-scale is important to the research as follows: balance of land-atmosphere interaction,land hydrology,improving the forecast accuracy of regional and GCM,monitoring of flooding and drought,assessment of the growth condition of corps,the natural and ecology Problems. Therefore the research on change and estimation of the soil moisture in regional and large-scale is very important,which also is a key international Problem.
The traditional surface observing network can not meet the study on soil moisture dynamic and successive variation in temporal and spatial scale. Furthermore, the widely used microwave measurement though have good penetration, there also have some existence disadvantages, such as the equipment is too heavy, Short-wave range of electromagnetic wave signal is greatly interfered by the atmosphere, and the wave band is difficult to control, which limit their automation in agriculture development. At present, there are a lot of ways to get the soil moisture information by using visual spectrum, near infrared, thermal infrared and other optical remote sensing measures. Although it has small size, easy imaging, short cycle, low cost and in favor of agriculture’s future popularization by using of optical remote sensing, this means mostly rely on high-altitude aircraft applications at the present time. Because the band of optical remote sensing can not penetrate cloud, so it has been limited in practical applications. The emergence of unmanned aerial vehicle resolved these issues.
As a new way to obtain information, unmanned aerial vehicle along with technology maturity, has been applied in a growing number of fields. Compared with normally used information capture aircraft and satellites, unmanned aerial vehicle is very fitful to the future of agriculture popularization with the chrematistics of low-cost, high accuracy, run a short cycle, easy to operate, etc., This study is based on this, using UAV as aircraft, visual spectrum and near infrared as a means of remote sensing measurements to study how to detect a certain region of the surface soil moisture information ,using Utah, United States for example. In this study, the main contents are as follows:
First of all, set up the image of many spatial analysis models and fractional order normalized difference vegetation index (NDVI). visual spectrum as well as near infrared and other information, collected back by unmanned aerial vehicle, are independent. The information associated with soil moisture is insufficient to achieve the prediction standard. Previous studies are analyzed through a variety of formulas, such as HIS, GRAY, NDVI, etc., which integrate this detected information so as to an accurate prediction standard of soil moisture. The purpose of this study by comparing the image characteristics of a variety of spatial analysis, is putting forward a fractional order normalized difference vegetation index, which can greatly improve contains information’s correlation coefficients between soil moisture and image, and guarantee the prediction accuracy by using the soil moisture model.
Secondly, propose image mosaic algorithm based on local gray-scale matching. There have been a lot of Mosaics Algorithms, which can complete a very good image mosaic mission. However, these algorithms need more time to run, which can not amount to real time requiring by Unmanned Aerial Vehicle (UAV) image mosaic. For this purpose, this paper proposed image mosaic algorithm based on local gray-scale matching. In the process of image mosaic, this study integrated UAV flight characteristics, which greatly narrowed the search area of image matching feature points. And grayscale images for rapid conversion of the image features, in this condition, feature points can accurately and quickly match, saving the operating costs and meeting the requirements of UAV image real-time mosaic.
Thirdly, complete the Calibration Algorithm for Unmanned Aerial Vehicle (UAV) image projection. Because Unmanned Aerial Vehicle (UAV) is impact by flight courses, wind speed and other uncertainty factors in the process of its flight, the images will have an uncertainty angle with the ground. This study proposed the Calibration Algorithm for Unmanned Aerial Vehicle (UAV) image projection, which can complete image angle correction automatically, based on the information recording by Unmanned Aerial Vehicle (UAV) immediately, and laid a foundation for the image mosaic.
Fourthly, propose image segmentation model based on cluster analysis algorithm. It is very important to the soil moisture description as well as information extraction of agricultural automatic irrigation throng determining the region border of image. The usual segmentation algorithms are classified according by the distribution probability of pixels or image features and spatial analysis, which results are rough. In this study, it will be better to zoning boundaries through the cluster analysis algorithm, and according to the gradient of boundary points, as well as its distribution trends.
传统的地面观测站网络不能满足大尺度土壤水分的时间、空间连续动态变化研究的需要。而现在广泛应用的微波测量方式虽然穿透力强,也存在设备过于沉重、短波范围内电测波信号受大气干扰比较大、波段难以控制等缺点,限制了其在农业自动化方面的发展。目前也有很多用可见光-近红外-热红外等光学遥感手段来获取土壤湿度时空分布信息,采用光学遥感具有体积小、成像简单、周期短、费用低等优点,利于将来的农业普及。但是这种手段在目前的应用中大多依靠高空飞行器负载,因为光学遥感的波段无法穿透云层,所以在实际应用中受到了限制。而无人机的出现解决了这些问题。
无人机作为一种新型的信息获取方式,伴随着技术的逐渐成熟,已经在越来越多的领域得到了应用。与通常信息获取使用的飞行器以及卫星相比,无人机具有费用低、精确度高、运行周期短、易于操作等优点,非常适合将来在农业当中普及。本研究正是基于此,选用无人机作为飞行器,可见光-近红外光作为遥感测量的手段,以美国犹他州为例研究如何探测一定区域内土壤表层的湿度信息的问题。本研究的主要内容如下:
首先,建立了图像的多空间分析模型以及分数阶植被归一化公式。用无人机采集回的可见光以及近红外光等信息都是独立的,与土壤湿度的信息的关联都不足以达到预测的标准。以往的研究都是通过各种分析公式,比如HIS、GRAY、NDVI等,将探测到的信息融合起来,达到了较为准确预测土壤湿度的标准。而本研究通过对比图像的各种空间分析特性,提出了分数阶的植被归一化公式,能够大大提高土壤湿度与图像包含信息的相关系数,保证利用建立的模型预测土壤湿度的准确性。
其次,提出了根据无人机图像特点的的图像拼接算法。很多已有的拼接算法已经能够很好的完成图像的镶嵌任务。但是,这些算法运行需要较多的时间,达不到无人机图像拼接的实时要求。出于这个目的,本研究提出了基于局部灰度匹配的图像拼接算法,在图像拼接的过程中融合无人机的飞行特征,大大缩小了图像匹配特征点的搜索范围。并且图像灰度为快速转换的图像特性,在此条件下特征点能够准确、快速匹配,节约了运行成本,达到了无人机图像实时拼接的要求。
第三,完成了无人机图像几何校正算法。由于无人机在飞行的过程受到航线、风速等不确定的影响,所拍摄的图像与地面会产生不确定的夹角。本研究提出了无人机图像几何校正算法,能够根据无人机即时记录下的信息,自动完成图像角度的校正,为图像拼接打下基础。
第四,提出了基于聚类分析算法的图像分割模型。图像中确定区域的边界对土壤湿度的描述以及农业自动灌溉信息的提取至关重要。通常的分割算法都是基于着色点的分布概率或者图像的特征空间分析进行像素点归类,结果比较粗糙。本研究通过聚类分析算法,根据边界点的梯度以及分布趋势,划分区域边界,效果较好。
最后,通过实验验证了之前提出的算法,并且对预测模型的精度进行了分析。结果表明,该模型达到了预测的要求,可以在实际中应用。
Soil moisture is one of essential Parameters in the study of climatology,hydrology,ecology and agriculture,which dominates the transportation and balance of water and heat between lands and air directly. The change of the soil moisture will lead to change of the thermotics characteristic of soil and optical properties of surface,even to the change of climate. The information of the change of land soil moisture in regional and large-scale is important to the research as follows: balance of land-atmosphere interaction,land hydrology,improving the forecast accuracy of regional and GCM,monitoring of flooding and drought,assessment of the growth condition of corps,the natural and ecology Problems. Therefore the research on change and estimation of the soil moisture in regional and large-scale is very important,which also is a key international Problem.
The traditional surface observing network can not meet the study on soil moisture dynamic and successive variation in temporal and spatial scale. Furthermore, the widely used microwave measurement though have good penetration, there also have some existence disadvantages, such as the equipment is too heavy, Short-wave range of electromagnetic wave signal is greatly interfered by the atmosphere, and the wave band is difficult to control, which limit their automation in agriculture development. At present, there are a lot of ways to get the soil moisture information by using visual spectrum, near infrared, thermal infrared and other optical remote sensing measures. Although it has small size, easy imaging, short cycle, low cost and in favor of agriculture’s future popularization by using of optical remote sensing, this means mostly rely on high-altitude aircraft applications at the present time. Because the band of optical remote sensing can not penetrate cloud, so it has been limited in practical applications. The emergence of unmanned aerial vehicle resolved these issues.
As a new way to obtain information, unmanned aerial vehicle along with technology maturity, has been applied in a growing number of fields. Compared with normally used information capture aircraft and satellites, unmanned aerial vehicle is very fitful to the future of agriculture popularization with the chrematistics of low-cost, high accuracy, run a short cycle, easy to operate, etc., This study is based on this, using UAV as aircraft, visual spectrum and near infrared as a means of remote sensing measurements to study how to detect a certain region of the surface soil moisture information ,using Utah, United States for example. In this study, the main contents are as follows:
First of all, set up the image of many spatial analysis models and fractional order normalized difference vegetation index (NDVI). visual spectrum as well as near infrared and other information, collected back by unmanned aerial vehicle, are independent. The information associated with soil moisture is insufficient to achieve the prediction standard. Previous studies are analyzed through a variety of formulas, such as HIS, GRAY, NDVI, etc., which integrate this detected information so as to an accurate prediction standard of soil moisture. The purpose of this study by comparing the image characteristics of a variety of spatial analysis, is putting forward a fractional order normalized difference vegetation index, which can greatly improve contains information’s correlation coefficients between soil moisture and image, and guarantee the prediction accuracy by using the soil moisture model.
Secondly, propose image mosaic algorithm based on local gray-scale matching. There have been a lot of Mosaics Algorithms, which can complete a very good image mosaic mission. However, these algorithms need more time to run, which can not amount to real time requiring by Unmanned Aerial Vehicle (UAV) image mosaic. For this purpose, this paper proposed image mosaic algorithm based on local gray-scale matching. In the process of image mosaic, this study integrated UAV flight characteristics, which greatly narrowed the search area of image matching feature points. And grayscale images for rapid conversion of the image features, in this condition, feature points can accurately and quickly match, saving the operating costs and meeting the requirements of UAV image real-time mosaic.
Thirdly, complete the Calibration Algorithm for Unmanned Aerial Vehicle (UAV) image projection. Because Unmanned Aerial Vehicle (UAV) is impact by flight courses, wind speed and other uncertainty factors in the process of its flight, the images will have an uncertainty angle with the ground. This study proposed the Calibration Algorithm for Unmanned Aerial Vehicle (UAV) image projection, which can complete image angle correction automatically, based on the information recording by Unmanned Aerial Vehicle (UAV) immediately, and laid a foundation for the image mosaic.
Fourthly, propose image segmentation model based on cluster analysis algorithm. It is very important to the soil moisture description as well as information extraction of agricultural automatic irrigation throng determining the region border of image. The usual segmentation algorithms are classified according by the distribution probability of pixels or image features and spatial analysis, which results are rough. In this study, it will be better to zoning boundaries through the cluster analysis algorithm, and according to the gradient of boundary points, as well as its distribution trends.
引文
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