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CN110110641B - A UAV monitoring method and system for watershed flood scene - Google Patents

A UAV monitoring method and system for watershed flood scene Download PDF

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CN110110641B
CN110110641B CN201910355047.2A CN201910355047A CN110110641B CN 110110641 B CN110110641 B CN 110110641B CN 201910355047 A CN201910355047 A CN 201910355047A CN 110110641 B CN110110641 B CN 110110641B
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CN110110641A (en
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雷添杰
付健
张亚珍
程子懿
汪洋
刘中伟
杨轶龙
周沅璟
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China Institute of Water Resources and Hydropower Research
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Abstract

本发明公开了一种流域性洪涝场景的无人机监测方法及系统。本发明基于第一无人机系统和多个第二无人机系统,获取流域性洪涝场景的全景视频、遥感影像和点云数据影像,然后根据全景视频对洪涝场景进行实时监测,并在遥感影像和点云数据影像中提取淹没的土地利用类型,以及每种土地利用类型的淹没范围和淹没深度,实现了及时、准确且低成本的流域性洪涝灾害场景检测。

Figure 201910355047

The invention discloses an unmanned aerial vehicle monitoring method and system for a watershed flood scene. Based on the first unmanned aerial vehicle system and a plurality of second unmanned aerial vehicle systems, the present invention obtains panoramic videos, remote sensing images and point cloud data images of the flooding scene in the basin, and then performs real-time monitoring of the flooding scene according to the panoramic video, and performs remote sensing on the flooding scene in real time. The submerged land use types, as well as the submerged range and submergence depth of each land use type are extracted from the images and point cloud data images, enabling timely, accurate and low-cost detection of watershed flood disaster scenarios.

Figure 201910355047

Description

一种流域性洪涝场景的无人机监测方法及系统A UAV monitoring method and system for watershed flood scene

技术领域technical field

本发明涉及洪涝监测领域,特别涉及一种流域性洪涝场景的无人机监测方法及系统。The invention relates to the field of flood monitoring, in particular to a method and system for unmanned aerial vehicle monitoring in a watershed flood scene.

背景技术Background technique

中国是洪涝灾害频发的国家,其中流域性洪涝灾害尤为频繁,严重危险广大人民群众的生命财产安全。例如,1991年,江淮地区遭受特大流域性洪涝灾害,致使2.3亿人受灾,1930万人被洪水围困,受灾农田425万公顷,毁坏房屋605万间,减产粮食200多亿公斤,直接经济损失达500多亿元(中国国际减灾十年委员会,1991)。因此,深入开展流域性洪涝灾害场景检测研究,不仅是防灾减灾研究的重要组成部分,对流域防洪规划、江河洪泛区土地的合理利用以及区域经济持续发展等方面均具重要意义。China is a country with frequent flood disasters, among which watershed flood disasters are particularly frequent, seriously endangering the lives and property of the general public. For example, in 1991, the Jianghuai region suffered from a huge watershed flood disaster, which affected 230 million people, 19.3 million people were besieged by floods, 4.25 million hectares of farmland were affected, 6.05 million houses were destroyed, more than 20 billion kilograms of grain were reduced, and the direct economic loss reached More than 50 billion yuan (China International Decade for Disaster Reduction Committee, 1991). Therefore, in-depth research on the detection of flood disaster scenarios in river basins is not only an important part of disaster prevention and mitigation research, but also has great significance for flood control planning in river basins, rational use of land in river floodplains, and sustainable development of regional economy.

传统的灾害调查与评估主要依据于地面调查,灾情数据受到人为因素影响而“水分较大”,真实性不高。卫星遥感技术虽然在灾害调查与评估方面具有较大的客观性,但卫星遥感技术因为卫星重访周期限制、云覆盖影响的因素,在时效性方面限制较大。载人飞机具有自主性强、机动灵活等特点,至今仍然是一种重要的遥感平台,在自然灾害评估调查等方面还经常发挥着难以替代的作用,但由于使用成本较大,限制了其广泛使用。如何提供及时、准确且低成本的流域性洪涝灾害场景检测成为一个亟待解决的技术问题。The traditional disaster investigation and assessment is mainly based on the ground investigation, and the disaster data is affected by human factors and the "water is large", and the authenticity is not high. Although satellite remote sensing technology has greater objectivity in disaster investigation and assessment, satellite remote sensing technology is limited in terms of timeliness due to the limitation of satellite revisit cycle and factors affected by cloud coverage. Manned aircraft has the characteristics of strong autonomy and flexibility, and it is still an important remote sensing platform, and it often plays an irreplaceable role in natural disaster assessment and investigation. use. How to provide timely, accurate and low-cost flood disaster scene detection has become an urgent technical problem to be solved.

发明内容SUMMARY OF THE INVENTION

本发明的目的是提供一种流域性洪涝场景的无人机监测方法及系统,以实现及时、准确且低成本的流域性洪涝灾害场景检测。The purpose of the present invention is to provide a UAV monitoring method and system for a watershed flood scene, so as to realize timely, accurate and low-cost detection of a watershed flood disaster scene.

为实现上述目的,本发明提供了如下方案:For achieving the above object, the present invention provides the following scheme:

本发明提供一种流域性洪涝场景的无人机监测方法,所述监测方法包括如下步骤:The present invention provides a UAV monitoring method for a watershed flood scene, and the monitoring method includes the following steps:

通过第一无人机系统获取拍摄流域性洪涝场景的全景视频;Obtain panorama videos of watershed flood scenes through the first unmanned aerial system;

根据所述全景视频实时监测流域性洪涝的实际场景;Real-time monitoring of the actual scene of watershed flooding according to the panoramic video;

通过多个第二无人机系统,获取多个遥感影像集和多个点云数据影像集;每个第二无人机系统获取的遥感影像集包括所述第二无人机系统在不同的时间点获取的多个遥感影像;每个第二无人机系统获取的点云数据影像集包括所述第二无人机系统在不同的时间点获取的多个点云数据影像;Obtain multiple remote sensing image sets and multiple point cloud data image sets through multiple second unmanned aerial systems; the remote sensing image sets obtained by each second unmanned aerial system include the second unmanned aerial system in different a plurality of remote sensing images acquired at a time point; the point cloud data image set acquired by each second UAV system includes a plurality of point cloud data images acquired by the second UAV system at different time points;

将多个所述点云数据影像集中的点云数据影像拼接成合成点云数据影像;Stitching the point cloud data images in a plurality of the point cloud data image sets into a composite point cloud data image;

根据所述合成点云数据影像与地理空间数据云数据库中的土地覆盖点云数据遥感影像进行影像配准直观对比,确定淹没的土地利用类型;Perform an intuitive comparison of image registration according to the synthetic point cloud data image and the remote sensing image of the land cover point cloud data in the geospatial data cloud database, and determine the submerged land use type;

将多个遥感影像集中的遥感影像拼接成合成遥感影像;Splicing remote sensing images from multiple remote sensing image sets into synthetic remote sensing images;

根据所述合成遥感影像确定每种土地利用类型的淹没范围和淹没深度。The submerged extent and submerged depth of each land use type are determined according to the synthetic remote sensing image.

可选的,所述将多个遥感影像集中的遥感影像拼接成合成遥感影像,具体包括:Optionally, the splicing of remote sensing images in multiple remote sensing image sets into a synthetic remote sensing image specifically includes:

采用SURF算法和HSI(Hue-Saturation-Intensity(Lightness))颜色模型对多个遥感影像集中的遥感影像进行粗匹配,得到多个粗匹配点;Using SURF algorithm and HSI (Hue-Saturation-Intensity (Lightness)) color model to roughly match remote sensing images in multiple remote sensing image sets to obtain multiple rough matching points;

采用随机抽样一致性算法对多个所述粗匹配点进行提纯,得到多个提纯后的粗匹配点;A random sampling consistency algorithm is used to purify a plurality of the rough matching points to obtain a plurality of purified rough matching points;

采用最小二乘法对多个所述提纯后的粗匹配点进行精匹配,得到多个精匹配点;The least squares method is used to perform fine matching on a plurality of the purified rough matching points to obtain a plurality of fine matching points;

基于多个所述精匹配点,采用插值方法将多个遥感影像集中的遥感影像拼接成合成遥感影像。Based on a plurality of the precise matching points, an interpolation method is used to stitch the remote sensing images in the plurality of remote sensing image sets into a synthetic remote sensing image.

可选的,所述采用SURF算法和HSI颜色模型对多个遥感影像集中的遥感影像进行粗匹配,得到多个粗匹配点,具体包括:Optionally, the SURF algorithm and the HSI color model are used to perform rough matching on remote sensing images in multiple remote sensing image sets to obtain multiple rough matching points, specifically including:

构建每个遥感影像的尺度空间;Construct the scale space of each remote sensing image;

建立并求解每个遥感影像的尺度空间的黑塞矩阵,得到每个遥感影像的多个特征点;Establish and solve the Hessian matrix of the scale space of each remote sensing image, and obtain multiple feature points of each remote sensing image;

构造每个特征点的描述子;Construct the descriptor of each feature point;

根据所述HSI颜色模型,将每个特征点的色彩数据添加至所述特征点的描述子,得到每个特征点的色彩描述子;According to the HSI color model, the color data of each feature point is added to the descriptor of the feature point to obtain the color descriptor of each feature point;

根据每个特征点的色彩描述子,确定所述特征点是否为遥感影像重叠区域的特征点,选取重叠区域的特征点作为重叠的遥感影像的粗匹配点。According to the color descriptor of each feature point, it is determined whether the feature point is the feature point in the overlapping area of the remote sensing image, and the feature point in the overlapping area is selected as the rough matching point of the overlapping remote sensing image.

可选的,所述根据所述合成遥感影像确定每种土地利用类型的淹没范围和淹没深度,具体包括:Optionally, determining the submerged range and submerged depth of each land use type according to the synthetic remote sensing image specifically includes:

根据所述合成遥感影像,建立流域洪涝场景的数字高程模型;According to the synthetic remote sensing image, establish a digital elevation model of the flood scene in the basin;

将所述流域洪涝场景的数字高程模型与所述流域的洪涝前的数字高程模型对比,确定每种土地利用类型的淹没范围和淹没深度。The digital elevation model of the flooding scene in the watershed is compared with the digital elevation model of the watershed before the flooding, and the submerged range and submerged depth of each land use type are determined.

可选的,所述土地利用类型包括居民地、道路、桥梁和耕地中的一种或几种。Optionally, the land use type includes one or more of residential land, road, bridge and cultivated land.

一种流域性洪涝场景的无人机监测系统,所述监测系统包括:An unmanned aerial vehicle monitoring system for a watershed flood scenario, the monitoring system includes:

第一无人机系统、多个第二无人机系统和地面控制及数据处理中心;a first UAS, a plurality of second UAS, and a ground control and data processing center;

所述第一无人机系统和多个所述第二无人机系统与所述地面控制及数据处理中心无线连接;the first unmanned aerial vehicle system and a plurality of the second unmanned aerial vehicle systems are wirelessly connected to the ground control and data processing center;

所述第一无人机系统包括第一无人机、视频相机和第一无线数据传输模块,所述视频相机和所述第一无线数据传输模块安装在所述第一无人机上;The first unmanned aerial vehicle system includes a first unmanned aerial vehicle, a video camera and a first wireless data transmission module, and the video camera and the first wireless data transmission module are installed on the first unmanned aerial vehicle;

所述视频相机通过所述第一无线数据传输模块与所述地面控制及数据处理中心无线连接;所述视频相机用于拍摄所述流域性洪涝场景的全景视频,并将所述全景视频通过所述第一无线数据传输模块发送给所述地面控制及数据处理中心;The video camera is wirelessly connected to the ground control and data processing center through the first wireless data transmission module; the video camera is used to shoot a panoramic video of the watershed flood scene, and transmit the panoramic video through all the sending the first wireless data transmission module to the ground control and data processing center;

所述第二无人机系统包括第二无人机、测绘相机、激光雷达和第二无线数据传输模块;The second unmanned aerial vehicle system includes a second unmanned aerial vehicle, a surveying and mapping camera, a lidar and a second wireless data transmission module;

所述测绘相机、所述激光雷达和所述第二无线数据传输模块安装在所述第二无人机上;the surveying and mapping camera, the lidar and the second wireless data transmission module are mounted on the second unmanned aerial vehicle;

所述测绘相机和所述激光雷达分别通过所述第二无线数据传输模块与所述地面控制及数据处理中心无线连接,所述测绘相机用于获取流域性洪涝场景的遥感影像集,并将所述遥感影像集通过所述无线数据传输模块发送给所述地面控制及数据处理中心;所述激光雷达用于获取点云数据影像集,并将所述点云数据影像集通过第二无线数据传输模块发送给所述地面控制及数据处理中心;The surveying and mapping camera and the lidar are respectively wirelessly connected to the ground control and data processing center through the second wireless data transmission module. The remote sensing image set is sent to the ground control and data processing center through the wireless data transmission module; the lidar is used to obtain the point cloud data image set, and transmit the point cloud data image set through the second wireless data The module is sent to the ground control and data processing center;

所述地面控制及数据处理中心用于根据所述场景视频、所述遥感影像集和所述点云数据影像集,获取流域性洪涝的实际场景,淹没的土地利用类型,以及每种土地利用类型的淹没范围和淹没深度;The ground control and data processing center is used to obtain the actual scene of watershed flood, the submerged land use type, and each land use type according to the scene video, the remote sensing image set and the point cloud data image set. the submerged range and submerged depth;

所述第一无人机系统的第一无人机和多个所述第二无人机系统的第二无人机分别与所述地面控制及数据处理中心无线连接;所述地面控制及数据处理中心还用于控制所述第一无人机和多个所述第二无人机的飞行。The first drone of the first drone system and the second drones of the second drone systems are respectively wirelessly connected to the ground control and data processing center; the ground control and data The processing center is also used for controlling the flight of the first drone and the plurality of second drones.

可选的,所述第一无人机在多个所述第二无人机的上空飞行;多个所述第二无人机在同一高度并排等间距的飞行。Optionally, the first unmanned aerial vehicle flies over a plurality of the second unmanned aerial vehicles; and the multiple second unmanned aerial vehicles fly side by side at the same height and at equal intervals.

可选的,所述第一无人机和多个所述第二无人机相互平行且同步的沿“8”字形飞行路线飞行。Optionally, the first unmanned aerial vehicle and the plurality of second unmanned aerial vehicles are parallel to each other and synchronously fly along the "8"-shaped flight route.

可选的,所述第一无人机系统还包括:红外视频相机,所述红外视频相机安装在所述第一无人机上,所述红外视频相机通过所述第一无线数据传输模块与所述地面控制及数据处理中心连接。Optionally, the first unmanned aerial vehicle system further includes: an infrared video camera, the infrared video camera is installed on the first unmanned aerial vehicle, and the infrared video camera communicates with the all drones through the first wireless data transmission module. The above ground control and data processing center connection.

可选的,所述第二无人机系统还包括:红外摄影相机,所述红外摄影相机安装在所述第二无人机上,所述红外摄影相机通过所述无线数据传输模块与所述数据处理中心连接。Optionally, the second unmanned aerial vehicle system further includes: an infrared photographic camera, the infrared photographic camera is installed on the second unmanned aerial vehicle, and the infrared photographic camera communicates with the data through the wireless data transmission module. Processing center connection.

根据本发明提供的具体实施例,本发明公开了以下技术效果:According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

本发明公开了一种流域性洪涝场景的无人机监测方法及系统。本发明基于第一无人机系统和多个第二无人机系统,获取流域性洪涝场景的全景视频、遥感影像和点云数据影像,然后根据全景视频对洪涝场景进行实时监测,并在遥感影像和点云数据影像中提取淹没的土地利用类型,以及每种土地利用类型的淹没范围和淹没深度,实现了及时、准确且低成本的流域性洪涝灾害场景检测。The invention discloses an unmanned aerial vehicle monitoring method and system for a watershed flood scene. Based on the first unmanned aerial vehicle system and a plurality of second unmanned aerial vehicle systems, the present invention obtains panoramic videos, remote sensing images and point cloud data images of watershed flood scenes, and then performs real-time monitoring on the flood scenes according to the panoramic videos, and performs remote sensing on the flood scenes in real time. The submerged land use types, as well as the submerged range and submerged depth of each land use type are extracted from the images and point cloud data images, enabling timely, accurate and low-cost detection of watershed flood disaster scenarios.

附图说明Description of drawings

为了更清楚地说明本发明实施例或现有技术中的技术方案,下面将对实施例中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本发明的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动性的前提下,还可以根据这些附图获得其他的附图。In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings required in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some of the present invention. In the embodiments, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative labor.

图1为本发明提供的一种流域性洪涝场景的无人机监测方法的流程图;Fig. 1 is the flow chart of the unmanned aerial vehicle monitoring method of a kind of watershed flood scene provided by the present invention;

图2为本发明提供的将多个遥感影像集中的遥感影像拼接成合成遥感影像的流程图;2 is a flow chart of splicing remote sensing images in a plurality of remote sensing image collections into a synthetic remote sensing image provided by the present invention;

图3为本发明提供的一种流域性洪涝场景的无人机监测系统的结构图。FIG. 3 is a structural diagram of a UAV monitoring system for a watershed flood scenario provided by the present invention.

具体实施方式Detailed ways

本发明的目的是提供一种流域性洪涝场景的无人机监测方法及系统,以实现及时、准确且低成本的流域性洪涝灾害场景检测。The purpose of the present invention is to provide a UAV monitoring method and system for a watershed flood scene, so as to realize timely, accurate and low-cost detection of a watershed flood disaster scene.

为使本发明的上述目的、特征和优点能够更加明显易懂,下面结合附图和具体实施方式对发明作进一步详细的说明。In order to make the above objects, features and advantages of the present invention more clearly understood, the invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

如图1所示,一种流域性洪涝场景的无人机监测方法,所述监测方法包括如下步骤:As shown in Figure 1, a UAV monitoring method for a watershed flood scene, the monitoring method includes the following steps:

步骤101,通过第一无人机系统获取拍摄流域性洪涝场景的全景视频。In step 101, a panoramic video of a flooded scene in a watershed is obtained through the first unmanned aerial system.

步骤102,根据所述全景视频实时监测流域性洪涝的实际场景。Step 102 , monitor the actual scene of watershed flood in real time according to the panoramic video.

步骤103,通过多个第二无人机系统,获取多个遥感影像集和多个点云数据影像集;每个第二无人机系统获取的遥感影像集包括所述第二无人机系统在不同的时间点获取的多个遥感影像;每个第二无人机系统获取的点云数据影像集包括所述第二无人机系统在不同的时间点获取的多个点云数据影像。Step 103: Obtain multiple remote sensing image sets and multiple point cloud data image sets through multiple second unmanned aerial systems; the remote sensing image sets obtained by each second unmanned aerial system include the second unmanned aerial system Multiple remote sensing images acquired at different time points; the point cloud data image set acquired by each second UAV system includes multiple point cloud data images acquired by the second UAV system at different time points.

步骤104,将多个所述点云数据影像集中的点云数据影像拼接成合成点云数据影像。其拼接方法与将多个遥感影像集中的遥感影像拼接成合成遥感影像的方法相同。Step 104 , splicing the point cloud data images in the plurality of point cloud data image sets into a composite point cloud data image. The splicing method is the same as that of splicing remote sensing images in multiple remote sensing image sets into synthetic remote sensing images.

步骤105,根据所述合成点云数据影像与地理空间数据云数据库中的土地覆盖点云数据遥感影像进行影像配准直观对比,确定淹没的土地利用类型。Step 105 , perform an intuitive comparison of image registration according to the synthetic point cloud data image and the remote sensing image of the land cover point cloud data in the geospatial data cloud database, and determine the submerged land use type.

步骤106,将多个遥感影像集中的遥感影像拼接成合成遥感影像。如图2所示,所述将多个所述点云数据影像集中的点云数据影像拼接成合成点云数据影像,具体包括:采用SURF算法和HSI颜色模型对多个遥感影像集中的遥感影像进行粗匹配,得到多个粗匹配点;采用随机抽样一致性算法对多个所述粗匹配点进行提纯,得到多个提纯后的粗匹配点;采用最小二乘法对多个所述提纯后的粗匹配点进行精匹配,得到多个精匹配点;基于多个所述精匹配点,采用插值方法将多个遥感影像集中的遥感影像拼接成合成遥感影像。Step 106, splicing the remote sensing images in the multiple remote sensing image sets into a synthetic remote sensing image. As shown in FIG. 2 , the splicing of point cloud data images in a plurality of the point cloud data image sets into a synthetic point cloud data image specifically includes: using the SURF algorithm and the HSI color model to synthesize the remote sensing images in the multiple remote sensing image sets Perform rough matching to obtain a plurality of rough matching points; adopt a random sampling consistency algorithm to purify a plurality of the rough matching points to obtain a plurality of purified rough matching points; adopt the least squares method to analyze the plurality of purified The coarse matching points are finely matched to obtain a plurality of fine matching points; based on the plurality of fine matching points, the remote sensing images in the plurality of remote sensing image sets are spliced into a synthetic remote sensing image by an interpolation method.

其中,采用SURF算法和HSI颜色模型对多个遥感影像集中的遥感影像进行粗匹配,得到多个粗匹配点,具体包括:构建每个遥感影像的尺度空间;建立并求解每个遥感影像的尺度空间的黑塞矩阵,得到每个遥感影像的多个特征点;构造每个特征点的描述子;根据所述HSI颜色模型,将每个特征点的色彩数据添加至所述特征点的描述子,得到每个特征点的色彩描述子;根据每个特征点的色彩描述子,确定所述特征点是否为遥感影像重叠区域的特征点,选取重叠区域的特征点作为重叠的遥感影像的粗匹配点。Among them, the SURF algorithm and the HSI color model are used to roughly match the remote sensing images in multiple remote sensing image sets to obtain multiple rough matching points, which include: constructing the scale space of each remote sensing image; establishing and solving the scale of each remote sensing image space Hessian matrix to obtain multiple feature points of each remote sensing image; construct the descriptor of each feature point; according to the HSI color model, add the color data of each feature point to the descriptor of the feature point , obtain the color descriptor of each feature point; according to the color descriptor of each feature point, determine whether the feature point is the feature point of the overlapping area of the remote sensing image, and select the feature point of the overlapping area as the rough matching of the overlapping remote sensing image. point.

步骤107,根据所述合成遥感影像确定每种土地利用类型的淹没范围和淹没深度;具体包括:根据所述合成遥感影像,建立流域洪涝场景的数字高程模型;将所述流域洪涝场景的数字高程模型与所述流域的洪涝前的数字高程模型对比,确定每种土地利用类型的淹没范围和淹没深度。Step 107: Determine the submerged range and submerged depth of each land use type according to the synthetic remote sensing image; specifically, it includes: establishing a digital elevation model of the flooded scene of the river basin according to the synthetic remote sensing image; The model is compared with the pre-flood digital elevation model of the watershed to determine the inundation extent and inundation depth of each land use type.

如图3所示,本发明还提供一种流域性洪涝场景的无人机监测系统,所述监测系统包括:As shown in FIG. 3 , the present invention also provides a UAV monitoring system for a watershed flood scenario, and the monitoring system includes:

第一无人机系统1、多个第二无人机系统2和地面控制及数据处理中心3;其中,所述第一无人机系统1和多个所述第二无人机系统2与所述地面控制及数据处理中心3无线连接;A first unmanned aerial system 1, a plurality of second unmanned aerial systems 2 and a ground control and data processing center 3; wherein, the first unmanned aerial system 1 and the plurality of second unmanned aerial systems 2 and The ground control and data processing center 3 is wirelessly connected;

所述第一无人机系统1包括第一无人机、视频相机和第一无线数据传输模块,所述视频相机和所述第一无线数据传输模块安装在所述第一无人机上;The first unmanned aerial vehicle system 1 includes a first unmanned aerial vehicle, a video camera and a first wireless data transmission module, and the video camera and the first wireless data transmission module are installed on the first unmanned aerial vehicle;

所述视频相机通过所述第一无线数据传输模块与所述地面控制及数据处理中心无线连接;所述视频相机用于拍摄所述流域性洪涝场景的全景视频,并将所述全景视频通过所述第一无线数据传输模块发送给所述地面控制及数据处理中心;The video camera is wirelessly connected to the ground control and data processing center through the first wireless data transmission module; the video camera is used to shoot a panoramic video of the watershed flood scene, and transmit the panoramic video through all the sending the first wireless data transmission module to the ground control and data processing center;

所述第二无人机系统2包括第二无人机、测绘相机、激光雷达和第二无线数据传输模块;The second unmanned aerial vehicle system 2 includes a second unmanned aerial vehicle, a surveying and mapping camera, a laser radar and a second wireless data transmission module;

所述测绘相机、所述激光雷达和所述第二无线数据传输模块安装在所述第二无人机上;the surveying and mapping camera, the lidar and the second wireless data transmission module are mounted on the second unmanned aerial vehicle;

所述测绘相机和所述激光雷达分别通过所述第二无线数据传输模块与所述地面控制及数据处理中心无线连接,所述测绘相机用于获取流域性洪涝场景的遥感影像集,并将所述遥感影像集通过所述无线数据传输模块发送给所述地面控制及数据处理中心;所述激光雷达用于获取点云数据影像集,并将所述点云数据影像集通过第二无线数据传输模块发送给所述地面控制及数据处理中心3;The surveying and mapping camera and the lidar are respectively wirelessly connected to the ground control and data processing center through the second wireless data transmission module. The remote sensing image set is sent to the ground control and data processing center through the wireless data transmission module; the lidar is used to obtain the point cloud data image set, and transmit the point cloud data image set through the second wireless data The module is sent to the ground control and data processing center 3;

所述地面控制及数据处理中心用于根据所述场景视频、所述遥感影像集和所述点云数据影像集,获取流域性洪涝的实际场景,淹没的土地利用类型,以及每种土地利用类型的淹没范围和淹没深度;The ground control and data processing center is used to obtain the actual scene of watershed flood, the submerged land use type, and each land use type according to the scene video, the remote sensing image set and the point cloud data image set. the submerged range and submerged depth;

所述第一无人机系统的第一无人机和多个所述第二无人机系统的第二无人机分别与所述地面控制及数据处理中心无线连接;所述地面控制及数据处理中心还用于控制所述第一无人机和多个所述第二无人机的飞行;具体的,控制所述第一无人机和多个所述第二无人机,使所述第一无人机在多个所述第二无人机的上空飞行;多个所述第二无人机在同一高度并排等间距的飞行,且使所述第一无人机和多个所述第二无人机相互平行且同步的沿“8”字形飞行路线飞行。The first drone of the first drone system and the second drones of the second drone systems are respectively wirelessly connected to the ground control and data processing center; the ground control and data The processing center is also used to control the flight of the first UAV and a plurality of the second UAVs; specifically, control the first UAV and the plurality of the second UAVs, so that all the The first UAV flies over a plurality of the second UAVs; a plurality of the second UAVs fly side by side at the same height at equal intervals, and the first UAV and the The second drones fly parallel to each other and synchronously along the "8"-shaped flight path.

为了保障夜间拍摄,所述第一无人机系统1还包括:红外视频相机,所述红外视频相机安装在所述第一无人机上,所述红外视频相机通过所述第一无线数据传输模块与所述地面控制及数据处理中心3连接。所述第二无人机系统2还包括:红外摄影相机,所述红外摄影相机安装在所述第二无人机上,所述红外摄影相机通过所述无线数据传输模块与所述数据处理中心3连接。In order to ensure nighttime shooting, the first UAV system 1 further includes: an infrared video camera, the infrared video camera is installed on the first UAV, and the infrared video camera passes through the first wireless data transmission module Connect with the ground control and data processing center 3 . The second unmanned aerial vehicle system 2 further includes: an infrared photographic camera, which is installed on the second unmanned aerial vehicle, and the infrared photographic camera communicates with the data processing center 3 through the wireless data transmission module. connect.

可见,本发明的无人机监测系统,考虑到流域性洪涝具有涉及范围广、爆发迅速的特点,因此利用无人机遥感进行监测时应充分考虑无人航空平台的续航时间和载荷能力。本发明采用两种不同型号的无人机分别完成大尺度宏观监测(第一无人机系统)和小尺度精细监测(多个第二无人机系统),两者配套使用,相辅相。具体安排是以续航时间较长和载荷能力较大的中程无人机(第一无人机系统)作为主要平台,完成大尺度宏观监测,以续航时间较短和载荷能力较小的近程无人机(多个第二无人机系统)作为辅助平台,实现重灾区流域精细监测。为满足洪水实时动态监测的需求,第一无人机上搭载高清监测视频相机,获取灾区的实时视频数据;第二无人机上搭载航空测绘相机,获取灾区的实时影像数据;为获取流域区域内DEM数据,可搭载高精度轻量化激光雷达。为保障夜间也能对灾区进行监测,也需配备相应的红外摄影相机和红外视频相机。本发明的提供的无人机监测系统的各个设备的参数如表1所示。It can be seen that the UAV monitoring system of the present invention takes into account the characteristics of wide coverage and rapid outbreak of watershed floods, so the endurance time and load capacity of the UAV platform should be fully considered when using UAV remote sensing for monitoring. The present invention adopts two different types of unmanned aerial vehicles to respectively complete large-scale macro monitoring (first unmanned aerial vehicle system) and small-scale fine monitoring (multiple second unmanned aerial vehicle systems), and the two are used together and complement each other. The specific arrangement is to use the medium-range UAV (the first UAV system) with a longer endurance time and a larger load capacity as the main platform to complete large-scale macro monitoring, and use the short-range UAV with a shorter endurance time and a smaller load capacity as the main platform. UAVs (multiple second UAV systems) are used as auxiliary platforms to realize fine monitoring of watersheds in hard-hit areas. In order to meet the needs of real-time dynamic monitoring of floods, the first drone is equipped with a high-definition monitoring video camera to obtain real-time video data of the disaster area; the second drone is equipped with an aerial mapping camera to obtain real-time image data of the disaster area; in order to obtain the DEM in the watershed area data, can be equipped with high-precision and lightweight lidar. In order to ensure that the disaster area can be monitored at night, corresponding infrared photography cameras and infrared video cameras are also required. The parameters of each device of the UAV monitoring system provided by the present invention are shown in Table 1.

表1流域性洪涝场景的无人机监测系统的各个设备的参数表Table 1 Parameter table of each equipment of the UAV monitoring system in the watershed flood scenario

Figure GDA0002720867500000081
Figure GDA0002720867500000081

本发明公开了一种流域性洪涝场景的无人机监测方法及系统。本发明基于第一无人机系统和多个第二无人机系统,获取流域性洪涝场景的全景视频、遥感影像和点云数据影像,然后根据全景视频对洪涝场景进行实时监测,并在遥感影像和点云数据影像中提取淹没的土地利用类型,以及每种土地利用类型的淹没范围和淹没深度,实现了及时、准确且低成本的流域性洪涝灾害场景检测。The invention discloses an unmanned aerial vehicle monitoring method and system for a watershed flood scene. Based on the first unmanned aerial vehicle system and a plurality of second unmanned aerial vehicle systems, the present invention obtains panoramic videos, remote sensing images and point cloud data images of watershed flood scenes, and then performs real-time monitoring on the flood scenes according to the panoramic videos, and performs remote sensing on the flood scenes in real time. The submerged land use types, as well as the submerged range and submerged depth of each land use type are extracted from the images and point cloud data images, enabling timely, accurate and low-cost detection of watershed flood disaster scenarios.

本说明书中各个实施例采用递进的方式描述,每个实施例重点说明的都是与其他实施例的不同之处,各个实施例之间相同相似部分互相参见即可。对于实施例公开的系统而言,由于其与实施例公开的方法相对应,所以描述的比较简单,相关之处参见方法部分说明即可。The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the various embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant part can be referred to the description of the method.

本文中应用了具体个例对发明的原理及实施方式进行了阐述,以上实施例的说明只是用于帮助理解本发明的方法及其核心思想,所描述的实施例仅仅是本发明的一部分实施例,而不是全部的实施例,基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。The principles and implementations of the invention are described herein by using specific examples. The descriptions of the above embodiments are only used to help understand the method and the core idea of the present invention, and the described embodiments are only a part of the embodiments of the present invention. , rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

Claims (10)

1.一种流域性洪涝场景的无人机监测方法,其特征在于,所述监测方法包括如下步骤:1. an unmanned aerial vehicle monitoring method of a watershed flood scene, is characterized in that, described monitoring method comprises the steps: 通过第一无人机系统获取拍摄流域性洪涝场景的全景视频;Obtain panorama videos of watershed flood scenes through the first unmanned aerial system; 根据所述全景视频实时监测流域性洪涝的实际场景;Real-time monitoring of the actual scene of watershed flooding according to the panoramic video; 通过多个第二无人机系统,获取多个遥感影像集和多个点云数据影像集;每个第二无人机系统获取的遥感影像集包括所述第二无人机系统在不同的时间点获取的多个遥感影像;每个第二无人机系统获取的点云数据影像集包括所述第二无人机系统在不同的时间点获取的多个点云数据影像;Obtain multiple remote sensing image sets and multiple point cloud data image sets through multiple second unmanned aerial systems; the remote sensing image sets obtained by each second unmanned aerial system include the second unmanned aerial system in different a plurality of remote sensing images acquired at a time point; the point cloud data image set acquired by each second UAV system includes a plurality of point cloud data images acquired by the second UAV system at different time points; 将多个所述点云数据影像集中的点云数据影像拼接成合成点云数据影像;Stitching the point cloud data images in a plurality of the point cloud data image sets into a composite point cloud data image; 根据所述合成点云数据影像与地理空间数据云数据库中的土地覆盖点云数据遥感影像进行影像配准直观对比,确定淹没的土地利用类型;Perform an intuitive comparison of image registration according to the synthetic point cloud data image and the remote sensing image of the land cover point cloud data in the geospatial data cloud database, and determine the submerged land use type; 将多个遥感影像集中的遥感影像拼接成合成遥感影像;Splicing remote sensing images from multiple remote sensing image sets into synthetic remote sensing images; 根据所述合成遥感影像确定每种土地利用类型的淹没范围和淹没深度。The submerged extent and submerged depth of each land use type are determined according to the synthetic remote sensing image. 2.根据权利要求1所述的流域性洪涝场景的无人机监测方法,其特征在于,所述将多个遥感影像集中的遥感影像拼接成合成遥感影像,具体包括:2. The unmanned aerial vehicle monitoring method of a watershed flood scene according to claim 1, wherein the described splicing of the remote sensing images in a plurality of remote sensing images into a synthetic remote sensing image, specifically comprises: 采用SURF算法和HSI颜色模型对多个遥感影像集中的遥感影像进行粗匹配,得到多个粗匹配点;Using SURF algorithm and HSI color model to roughly match remote sensing images in multiple remote sensing image sets to obtain multiple rough matching points; 采用随机抽样一致性算法对多个所述粗匹配点进行提纯,得到多个提纯后的粗匹配点;A random sampling consistency algorithm is used to purify a plurality of the rough matching points to obtain a plurality of purified rough matching points; 采用最小二乘法对多个所述提纯后的粗匹配点进行精匹配,得到多个精匹配点;The least squares method is used to perform fine matching on a plurality of the purified rough matching points to obtain a plurality of fine matching points; 基于多个所述精匹配点,采用插值方法将多个遥感影像集中的遥感影像拼接成合成遥感影像。Based on a plurality of the precise matching points, an interpolation method is used to stitch the remote sensing images in the plurality of remote sensing image sets into a synthetic remote sensing image. 3.根据权利要求2所述的一种流域性洪涝场景的无人机监测方法,其特征在于,所述采用SURF算法和HSI颜色模型对多个遥感影像集中的遥感影像进行粗匹配,得到多个粗匹配点,具体包括:3. the unmanned aerial vehicle monitoring method of a kind of watershed flooding scene according to claim 2, is characterized in that, described adopting SURF algorithm and HSI color model to carry out rough matching to the remote sensing images in a plurality of remote sensing image collections, obtains multiple remote sensing images. rough matching points, including: 构建每个遥感影像的尺度空间;Construct the scale space of each remote sensing image; 建立并求解每个遥感影像的尺度空间的黑塞矩阵,得到每个遥感影像的多个特征点;Establish and solve the Hessian matrix of the scale space of each remote sensing image, and obtain multiple feature points of each remote sensing image; 构造每个特征点的描述子;Construct the descriptor of each feature point; 根据所述HSI颜色模型,将每个特征点的色彩数据添加至所述特征点的描述子,得到每个特征点的色彩描述子;According to the HSI color model, the color data of each feature point is added to the descriptor of the feature point to obtain the color descriptor of each feature point; 根据每个特征点的色彩描述子,确定所述特征点是否为遥感影像重叠区域的特征点,选取重叠区域的特征点作为相互重叠的遥感影像的粗匹配点。According to the color descriptor of each feature point, it is determined whether the feature point is the feature point of the overlapping area of the remote sensing image, and the feature point of the overlapping area is selected as the rough matching point of the overlapping remote sensing image. 4.根据权利要求1所述的流域性洪涝场景的无人机监测方法,其特征在于,所述根据所述合成遥感影像确定每种土地利用类型的淹没范围和淹没深度,具体包括:4. The unmanned aerial vehicle monitoring method for a watershed flood scene according to claim 1, characterized in that, determining the submerged range and submerged depth of each land use type according to the synthetic remote sensing image, specifically comprising: 根据所述合成遥感影像,建立流域洪涝场景的数字高程模型;According to the synthetic remote sensing image, establish a digital elevation model of the flood scene in the basin; 将所述流域洪涝场景的数字高程模型与所述流域的洪涝前的数字高程模型对比,确定每种土地利用类型的淹没范围和淹没深度。The digital elevation model of the flooding scene in the watershed is compared with the digital elevation model of the watershed before the flooding, and the submerged range and submerged depth of each land use type are determined. 5.根据权利要求1所述的流域性洪涝场景的无人机监测方法,其特征在于,所述土地利用类型包括居民地、道路、桥梁和耕地中的一种或几种。5 . The UAV monitoring method for a watershed flood scene according to claim 1 , wherein the land use type includes one or more of residential land, road, bridge and cultivated land. 6 . 6.一种流域性洪涝场景的无人机监测系统,其特征在于,所述监测系统包括:6. An unmanned aerial vehicle monitoring system for a watershed flood scenario, wherein the monitoring system comprises: 第一无人机系统、多个第二无人机系统和地面控制及数据处理中心;a first UAS, a plurality of second UAS, and a ground control and data processing center; 所述第一无人机系统和多个所述第二无人机系统与所述地面控制及数据处理中心无线连接;the first unmanned aerial vehicle system and a plurality of the second unmanned aerial vehicle systems are wirelessly connected to the ground control and data processing center; 所述第一无人机系统包括第一无人机、视频相机和第一无线数据传输模块,所述视频相机和所述第一无线数据传输模块安装在所述第一无人机上;The first unmanned aerial vehicle system includes a first unmanned aerial vehicle, a video camera and a first wireless data transmission module, and the video camera and the first wireless data transmission module are installed on the first unmanned aerial vehicle; 所述视频相机通过所述第一无线数据传输模块与所述地面控制及数据处理中心无线连接;所述视频相机用于拍摄所述流域性洪涝场景的全景视频,并将所述全景视频通过所述第一无线数据传输模块发送给所述地面控制及数据处理中心;The video camera is wirelessly connected to the ground control and data processing center through the first wireless data transmission module; the video camera is used to shoot a panoramic video of the watershed flood scene, and transmit the panoramic video through all the sending the first wireless data transmission module to the ground control and data processing center; 所述第二无人机系统包括第二无人机、测绘相机、激光雷达和第二无线数据传输模块;The second unmanned aerial vehicle system includes a second unmanned aerial vehicle, a surveying and mapping camera, a lidar and a second wireless data transmission module; 所述测绘相机、所述激光雷达和所述第二无线数据传输模块安装在所述第二无人机上;the surveying and mapping camera, the lidar and the second wireless data transmission module are mounted on the second unmanned aerial vehicle; 所述测绘相机和所述激光雷达分别通过所述第二无线数据传输模块与所述地面控制及数据处理中心无线连接,所述测绘相机用于获取流域性洪涝场景的遥感影像集,并将所述遥感影像集通过所述无线数据传输模块发送给所述地面控制及数据处理中心;所述激光雷达用于获取点云数据影像集,并将所述点云数据影像集通过第二无线数据传输模块发送给所述地面控制及数据处理中心;The surveying and mapping camera and the lidar are respectively wirelessly connected to the ground control and data processing center through the second wireless data transmission module. The remote sensing image set is sent to the ground control and data processing center through the wireless data transmission module; the lidar is used to obtain the point cloud data image set, and transmit the point cloud data image set through the second wireless data The module is sent to the ground control and data processing center; 所述地面控制及数据处理中心用于根据所述场景视频、所述遥感影像集和所述点云数据影像集,获取流域性洪涝的实际场景,淹没的土地利用类型,以及每种土地利用类型的淹没范围和淹没深度;The ground control and data processing center is used to obtain the actual scene of watershed flood, the submerged land use type, and each land use type according to the scene video, the remote sensing image set and the point cloud data image set. the submerged range and submerged depth; 所述第一无人机系统的第一无人机和多个所述第二无人机系统的第二无人机分别与所述地面控制及数据处理中心无线连接;所述地面控制及数据处理中心还用于控制所述第一无人机和多个所述第二无人机的飞行。The first drone of the first drone system and the second drones of the second drone systems are respectively wirelessly connected to the ground control and data processing center; the ground control and data The processing center is also used for controlling the flight of the first drone and the plurality of second drones. 7.根据权利要求6所述的流域性洪涝场景的无人机监测系统,其特征在于,所述第一无人机在多个所述第二无人机的上空飞行;多个所述第二无人机在同一高度并排等间距的飞行。7. The UAV monitoring system for a watershed flood scenario according to claim 6, wherein the first UAV flies over a plurality of the second UAVs; Two drones fly side by side at the same height and at equal intervals. 8.根据权利要求7所述的流域性洪涝场景的无人机监测系统,其特征在于,所述第一无人机和多个所述第二无人机相互平行且同步的沿“8”字形飞行路线飞行。8 . The UAV monitoring system for a watershed flood scenario according to claim 7 , wherein the first UAV and a plurality of the second UAVs are parallel to each other and synchronized along the “8” Glyph flight path flight. 9.根据权利要求6所述的流域性洪涝场景的无人机监测系统,其特征在于,所述第一无人机系统还包括:红外视频相机,所述红外视频相机安装在所述第一无人机上,所述红外视频相机通过所述第一无线数据传输模块与所述地面控制及数据处理中心连接。9. The unmanned aerial vehicle monitoring system for a watershed flood scene according to claim 6, wherein the first unmanned aerial vehicle system further comprises: an infrared video camera, wherein the infrared video camera is installed on the first unmanned aerial vehicle. On the drone, the infrared video camera is connected to the ground control and data processing center through the first wireless data transmission module. 10.根据权利要求6所述的流域性洪涝场景的无人机监测系统,其特征在于,所述第二无人机系统还包括:红外摄影相机,所述红外摄影相机安装在所述第二无人机上,所述红外摄影相机通过所述无线数据传输模块与所述数据处理中心连接。10. The UAV monitoring system for a watershed flood scene according to claim 6, wherein the second UAV system further comprises: an infrared photographic camera, wherein the infrared photographic camera is installed on the second unmanned aerial vehicle. On the drone, the infrared camera is connected to the data processing center through the wireless data transmission module.
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