CN114612794B - A remote sensing identification method for land cover and planting structure in fragmented agricultural areas - Google Patents

A remote sensing identification method for land cover and planting structure in fragmented agricultural areas Download PDF

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CN114612794B
CN114612794B CN202210193796.1A CN202210193796A CN114612794B CN 114612794 B CN114612794 B CN 114612794B CN 202210193796 A CN202210193796 A CN 202210193796A CN 114612794 B CN114612794 B CN 114612794B
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徐旭
李欣怡
蒙慧敏
黄权中
黄冠华
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Abstract

The invention belongs to the field of agricultural remote sensing, and relates to a remote sensing identification method of a ground covering and planting structure of a finely divided agricultural area. According to the invention, the agricultural area and the non-agricultural area are classified by the support vector machine SVM of the supervision algorithm, and then the crop type is further classified and judged by adopting the decision tree algorithm based on the obtained agricultural area classification raster data. The method does not depend on measured data, greatly reduces classification time and economic cost, and effectively avoids negative influence of the quality of the sample training set of the traditional machine algorithm on classification results. Meanwhile, the method meets the land utilization drawing precision required by agricultural remote sensing application on the premise of ensuring the classification high efficiency.

Description

一种细碎化农业区土地覆被与种植结构的遥感识别方法A remote sensing identification method for land cover and planting structure in fragmented agricultural areas

技术领域Technical Field

本发明属于农业遥感领域,涉及一种细碎化农业区土地覆被与种植结构的遥感识别方法,采用机器学习算法提取土地利用,不依赖实测样本点,自动训练后得到高精度农业区土地利用栅格化数据。The present invention belongs to the field of agricultural remote sensing, and relates to a remote sensing identification method for land cover and planting structure in fragmented agricultural areas. The method uses a machine learning algorithm to extract land use, does not rely on measured sample points, and obtains high-precision agricultural area land use rasterized data after automatic training.

背景技术Background technique

准确及时地获取农业区土地覆被信息对监测农情,调整农业结构,估产和制定粮食政策等均具有重要意义。传统的田间调查方法费时费力,且难以获取大尺度区域土地利用信息。遥感技术能克服单点观测在复杂地表条件下空间代表性差的缺点,为区域土地利用识别提供多时相、多光谱及多角度的地表信息。Accurate and timely acquisition of land cover information in agricultural areas is of great significance for monitoring agricultural conditions, adjusting agricultural structure, estimating production and formulating food policies. Traditional field survey methods are time-consuming and labor-intensive, and it is difficult to obtain large-scale regional land use information. Remote sensing technology can overcome the shortcomings of single-point observations with poor spatial representativeness under complex surface conditions, and provide multi-temporal, multi-spectral and multi-angle surface information for regional land use identification.

目前土地利用遥感反演主要分为基于多源数据物候和极化特征及基于实测样本点分类两种方式。基于样本点的识别算法只需输入样本训练集,利用卫星遥感图像,即可快速实现大尺度区域土地利用反演。其中支持向量机(SVM)和随机森林等算法因其训练效率高,精度满足应用要求,被广泛应用于土地利用遥感识别,但该方法对样本精度要求高,且应用时需要大量实测样本,时间及金钱成本较高。同时对于光谱及纹理特征较为类似的作物来说,该类算法在识别时会出现作物类型混分的情况。基于多源物候及极化特征算法只需建立特定的分类规则,不需实测样本训练集,即可利用决策树算法实现土地利用反演。这种方法只针对于物候/极化特征差异十分明显的地物识别,且需对原始卫星图像光谱数据进行处理分析,计算得到物候/极化特征用于分类。总而言之,现有的分类算法在应用于卫星遥感图像时,单一分类算法运算时间长,对计算机配置要求高,需要大量实测样本,费时费力,因此难以实现大尺度土地利用识别。At present, the remote sensing inversion of land use is mainly divided into two methods: based on multi-source data phenology and polarization characteristics and based on measured sample point classification. The recognition algorithm based on sample points only needs to input the sample training set and use satellite remote sensing images to quickly realize large-scale regional land use inversion. Among them, algorithms such as support vector machine (SVM) and random forest are widely used in land use remote sensing identification because of their high training efficiency and accuracy that meets application requirements. However, this method has high requirements on sample accuracy and requires a large number of measured samples when applied, which is time and money costly. At the same time, for crops with similar spectral and texture characteristics, this type of algorithm will mix crop types during identification. The algorithm based on multi-source phenology and polarization characteristics only needs to establish specific classification rules, without the need for measured sample training sets, and can use the decision tree algorithm to realize land use inversion. This method is only for the identification of objects with very obvious differences in phenology/polarization characteristics, and it is necessary to process and analyze the original satellite image spectral data to calculate the phenology/polarization characteristics for classification. In summary, when the existing classification algorithms are applied to satellite remote sensing images, a single classification algorithm takes a long time to operate, has high requirements on computer configuration, requires a large number of measured samples, is time-consuming and labor-intensive, and therefore it is difficult to achieve large-scale land use identification.

在实际应用中,SVM、随机森林、决策树等各种分类器都具有其优缺点,因此本发明结合各种算法的优势,针对细碎化农业区不同地物的特点应用不同算法,取长补短,同时提高分类的效率以及精度。In practical applications, various classifiers such as SVM, random forest, decision tree, etc. have their advantages and disadvantages. Therefore, the present invention combines the advantages of various algorithms and applies different algorithms according to the characteristics of different landforms in fragmented agricultural areas, taking advantage of their strengths and making up for their weaknesses, while improving the efficiency and accuracy of classification.

发明内容Summary of the invention

针对上述技术问题,本发明的目的是提供一种细碎化农业区土地覆被与种植结构的遥感识别方法,通过卫星遥感图像光谱及纹理特征,以及识别农业区和非农业区效果较好的LSWI指数,利用机器学习算法实现农业区和非农业区的识别。在此基础上,针对农业区不同作物所具有的物候特征建立分类规则,利用NDVI时序数据计算得到的物候指标作为输入数据,基于决策树算法实现农业区不同作物的进一步划分,最终实现细碎化农业区高精度土地利用遥感识别。In view of the above technical problems, the purpose of the present invention is to provide a remote sensing identification method for land cover and planting structure in fragmented agricultural areas, which uses the spectral and texture characteristics of satellite remote sensing images, as well as the LSWI index, which is more effective in identifying agricultural and non-agricultural areas, to achieve the identification of agricultural and non-agricultural areas using a machine learning algorithm. On this basis, classification rules are established for the phenological characteristics of different crops in agricultural areas, and the phenological indicators calculated using NDVI time series data are used as input data. The further division of different crops in agricultural areas is achieved based on the decision tree algorithm, and finally high-precision land use remote sensing identification of fragmented agricultural areas is achieved.

为了实现上述目的,本发明提供了如下技术方案:In order to achieve the above object, the present invention provides the following technical solutions:

一种细碎化农业区土地覆被与种植结构的遥感识别方法,包括如下步骤:A remote sensing identification method for land cover and planting structure in fragmented agricultural areas comprises the following steps:

S1、利用支持向量机SVM监督学习算法对原始卫星图像进行农业区及非农业区识别:S1. Use the support vector machine (SVM) supervised learning algorithm to identify agricultural and non-agricultural areas in the original satellite images:

S1.1、采用双线性插值法将多张不同时期不同气象条件的原始卫星图像的每个波段重采样为统一的空间分辨率;选择一张八月初作物生长旺盛的原始卫星图像,并选取该图像中能较好分辨地物的可见光波段以及能判断植物生长状况的红边波段、近红外波段或短波红外波段作为分类识别波段;利用二阶概率统计方法用一个灰色调空间相关性矩阵,基于3×3的窗口和64灰度量化级别计算分类识别波段的纹理值;S1.1. Use bilinear interpolation to resample each band of multiple original satellite images of different periods and different meteorological conditions to a uniform spatial resolution; select an original satellite image of crops growing vigorously in early August, and select the visible light band that can better distinguish the ground objects and the red edge band, near infrared band or short-wave infrared band that can judge the growth status of plants in the image as the classification and identification bands; use the second-order probability statistics method to use a gray tone spatial correlation matrix, based on a 3×3 window and 64 gray quantization levels to calculate the texture value of the classification and identification band;

S1.2、利用生育期内多期晴空无云条件下的原始卫星图像,通过公式一得到陆地水体指数LSWI时序数据集,采用线性插值的方法将陆地水体指数LSWI时序数据集重采样为统一的时间分辨率,并以一预设阈值对数据集中的陆地水体指数LSWI时序数据进行重新赋值,大于预设阈值的像元赋值为0,小于预设阈值的像元赋值为1,将重新赋值的陆地水体指数LSWI时序数据依次相加求和,获得一个陆地水体指数LSWI重分类数据;S1.2. Using the original satellite images under clear and cloudless conditions for multiple periods during the growing season, the land water index LSWI time series data set is obtained through formula 1. The land water index LSWI time series data set is resampled to a uniform time resolution using a linear interpolation method, and the land water index LSWI time series data in the data set are reassigned with a preset threshold. The pixels greater than the preset threshold are assigned a value of 0, and the pixels less than the preset threshold are assigned a value of 1. The reassigned land water index LSWI time series data are added and summed in sequence to obtain a land water index LSWI reclassification data;

式中:LSWI为陆地水体指数;ρNIR表示近红外波段的反射率;ρSWIR表示短波红外波段的反射率;Where: LSWI is the land water index; ρ NIR represents the reflectivity of the near-infrared band; ρ SWIR represents the reflectivity of the short-wave infrared band;

S1.3、从Google Earth中选取土地利用样本集作为训练数据,利用支持向量机SVM监督分类算法对步骤S1.1得到的分类识别波段及纹理值和步骤S1.2获得的陆地水体指数LSWI重分类数据进行训练学习,计算得到农业区及非农业区的土地利用分类结果;S1.3, select a land use sample set from Google Earth as training data, use the support vector machine (SVM) supervised classification algorithm to train and learn the classification identification bands and texture values obtained in step S1.1 and the LSWI reclassification data obtained in step S1.2, and calculate the land use classification results of agricultural and non-agricultural areas;

S2、应用决策树算法对农业区进行进一步种植结构划分,得到最终农业区高精度土地利用数据:S2. Apply the decision tree algorithm to further divide the planting structure of the agricultural area and obtain the final high-precision land use data of the agricultural area:

S2.1、将步骤S1得到的土地利用分类结果转变为栅格数据并对其进行重新赋值,将农业区赋值为1,非农业区赋值为NoData;将重新赋值的栅格数据转变为矢量数据,获得只包含农业区的shp矢量数据;S2.1, converting the land use classification result obtained in step S1 into raster data and re-assigning it, assigning the agricultural area to 1 and the non-agricultural area to NoData; converting the re-assigned raster data into vector data to obtain shp vector data containing only the agricultural area;

S2.2、基于步骤S2.1得到的只包含农业区的shp矢量数据对生育期内多期晴空无云条件下的原始卫星图像进行裁剪,得到只包含农业区的原始卫星图像,并通过公式二计算得到归一化植被指数NDVI时序数据集,采用线性插值的方法将归一化植被指数NDVI时序数据集重采样为统一的时间分辨率;S2.2, based on the shp vector data containing only the agricultural area obtained in step S2.1, the original satellite images under clear and cloudless conditions for multiple periods during the growth period are cropped to obtain the original satellite images containing only the agricultural area, and the normalized vegetation index NDVI time series data set is calculated by formula 2, and the normalized vegetation index NDVI time series data set is resampled to a uniform time resolution by using a linear interpolation method;

式中:NDVI为归一化植被指数;ρNIR表示近红外波段的反射率;ρRed表示红光波段的反射率;Where: NDVI is the normalized vegetation index; ρ NIR represents the reflectance of the near-infrared band; ρ Red represents the reflectance of the red light band;

S2.3、利用Savitzky-Golay滤波器对步骤S2.2重采样后的归一化植被指数NDVI时序数据集进行平滑处理;基于平滑后的NDVI生育期过程曲线建立物候指标判断标准,对农业区每一个像元进行判断,最终得到物候指标栅格数据;所述物候指标栅格数据中包含生育期起始时间、生育期终止时间和生育期长度三个物候指标;S2.3, using Savitzky-Golay filter to smooth the normalized vegetation index NDVI time series data set after resampling in step S2.2; establishing a phenological index judgment standard based on the smoothed NDVI growth period process curve, judging each pixel in the agricultural area, and finally obtaining phenological index raster data; the phenological index raster data includes three phenological indicators: the start time of the growth period, the end time of the growth period and the length of the growth period;

所述物候指标判断标准的建立过程如下:The process of establishing the phenological index judgment standard is as follows:

确定在平滑后的NDVI生育期过程曲线的上升阶段、下降阶段以及NDVI设定值,当NDVI=设定值时,上升阶段所对应的时刻为生育期起始时间,下降阶段所对应的时刻为生育期终止时间,生育期起始时间与生育期终止时间内的时间长度为生育期长度;Determine the rising stage, falling stage and NDVI set value of the smoothed NDVI growth period process curve. When NDVI = set value, the moment corresponding to the rising stage is the start time of the growth period, the moment corresponding to the falling stage is the end time of the growth period, and the time length between the start time of the growth period and the end time of the growth period is the length of the growth period.

S2.4、根据不同作物的三个物候指标的值域范围,建立作物分类规则;基于作物分类规则,利用决策树算法对步骤S2.3获得的物候指标栅格数据中的每一个栅格进行判断,获得农业种植结构分类结果,最终使得每个栅格都被划分为某一类具体的作物类型,从而实现对农业区的作物识别;S2.4. Establish crop classification rules according to the value ranges of the three phenological indicators of different crops; based on the crop classification rules, use the decision tree algorithm to judge each grid in the phenological indicator grid data obtained in step S2.3, and obtain the agricultural planting structure classification results, so that each grid is finally divided into a specific crop type, thereby realizing crop identification in the agricultural area;

S3、将步骤S1得到的土地利用分类结果中非农业区与步骤S2获得的农业种植结构分类结果镶嵌到一起,得到一张带有分类标签色彩的农业种植区土地利用及种植结构分类图。S3. Inlay the non-agricultural areas in the land use classification result obtained in step S1 with the agricultural planting structure classification result obtained in step S2 to obtain a land use and planting structure classification map of the agricultural planting area with classification label colors.

所述步骤S1.2中,预设阈值为0.2。In step S1.2, the preset threshold is 0.2.

所述步骤S1.3中,土地利用分类结果中,农业区为农田,非农业区包括水体、沙丘、居民区和天然地。In the step S1.3, in the land use classification result, the agricultural area is farmland, and the non-agricultural area includes water bodies, sand dunes, residential areas and natural land.

所述步骤S2.3中,设定值为0.3。In step S2.3, the setting value is 0.3.

所述步骤S2.4得到的农业种植结构分类结果包括小麦、玉米、葵花和果蔬。The agricultural planting structure classification results obtained in step S2.4 include wheat, corn, sunflower, and fruits and vegetables.

所述步骤S2.4中,不同作物的三个物候指标的值域范围基于当地实际经验和实地调研分别确定。In step S2.4, the value ranges of the three phenological indicators of different crops are determined based on local practical experience and field research.

所述步骤S3中,农业种植区土地利用及种植结构分类图中的每一个像元都有一个分类,不同的值和颜色代表着不同的分类。In step S3, each pixel in the agricultural planting area land use and planting structure classification map has a classification, and different values and colors represent different classifications.

与现有技术相比,本发明的有益效果在于:Compared with the prior art, the present invention has the following beneficial effects:

1、本发明解决了实地样本获取数量有限,且实测样本质量要求较高,获取困难的问题,在无需实测样本的情况下,保证分类精度及稳定性。1. The present invention solves the problem that the number of field samples is limited, the quality requirements of measured samples are high, and they are difficult to obtain. The classification accuracy and stability are guaranteed without the need for measured samples.

2、本发明与传统算法相比,降低了时间成本及金钱成本,且可操作性较强,在应用于大区域大面积高精度遥感图像时,也可用普通硬件设备实现高精度土地利用识别,使得大区域的农业区土地利用自动获取成为可能。2. Compared with traditional algorithms, the present invention reduces time and money costs and has strong operability. When applied to large-area high-precision remote sensing images, it can also use ordinary hardware equipment to achieve high-precision land use identification, making it possible to automatically obtain land use in large agricultural areas.

附图说明BRIEF DESCRIPTION OF THE DRAWINGS

图1为本发明的细碎化农业区土地覆被与种植结构的遥感识别方法的流程图;FIG1 is a flow chart of a remote sensing identification method for land cover and planting structure in fragmented agricultural areas according to the present invention;

图2为本发明实施例的原始卫星遥感图像(在本描述中举例使用Sentinel-2历史图像,其他卫星原始图像均可);FIG2 is an original satellite remote sensing image according to an embodiment of the present invention (Sentinel-2 historical images are used as an example in this description, and other satellite original images may also be used);

图3为本发明实施例的原始卫星遥感图像纹理值计算过程示意图;3 is a schematic diagram of a process for calculating texture values of an original satellite remote sensing image according to an embodiment of the present invention;

图4为本发明实施例监督算法支持向量机SVM分类的计算过程及经过分类后得到的土地利用图像;FIG4 is a diagram showing the calculation process of the supervised algorithm support vector machine (SVM) classification and the land use image obtained after classification according to an embodiment of the present invention;

图5为本发明选用的物候指标示意图;FIG5 is a schematic diagram of phenological indicators selected by the present invention;

图6为本发明实施例用于作物分类的决策树分类规则;FIG6 is a decision tree classification rule for crop classification according to an embodiment of the present invention;

图7为本发明实施例对农业区进行具体种植结构分类后的图像;FIG7 is an image of an agricultural area after specific planting structure classification according to an embodiment of the present invention;

图8为本发明实施例最终得到的土地利用及种植结构分类图;FIG8 is a land use and planting structure classification diagram finally obtained in an embodiment of the present invention;

图9为分类结果中各个类别的图例。FIG9 is a legend of each category in the classification results.

具体实施方式Detailed ways

下面结合附图和实施例对本发明进行进一步说明。The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

如图1所示,一种细碎化农业区土地覆被与种植结构的遥感识别方法,包括如下步骤:As shown in FIG1 , a remote sensing identification method for land cover and planting structure in a fragmented agricultural area includes the following steps:

S1、利用支持向量机SVM监督学习算法对原始卫星图像进行农业区及非农业区识别:S1. Use the support vector machine (SVM) supervised learning algorithm to identify agricultural and non-agricultural areas in the original satellite images:

S1.1、采用双线性插值法将多张不同时期不同气象条件的原始卫星图像的每个波段重采样为统一的空间分辨率;选择一张八月初作物生长旺盛的原始卫星图像,并选取该图像中能较好分辨地物的可见光波段以及能判断植物生长状况的红边波段、近红外波段或短波红外波段作为分类识别波段;利用二阶概率统计方法用一个灰色调空间相关性矩阵,基于3×3的窗口和64灰度量化级别计算分类识别波段的纹理值;S1.1. Use bilinear interpolation to resample each band of multiple original satellite images of different periods and different meteorological conditions to a uniform spatial resolution; select an original satellite image of crops growing vigorously in early August, and select the visible light band that can better distinguish the ground objects and the red edge band, near infrared band or short-wave infrared band that can judge the growth status of plants in the image as the classification and identification bands; use the second-order probability statistics method to use a gray tone spatial correlation matrix, based on a 3×3 window and 64 gray quantization levels to calculate the texture value of the classification and identification band;

S1.2、利用生育期内多期晴空无云条件下的原始卫星图像,通过公式一得到陆地水体指数LSWI时序数据集,采用线性插值的方法将陆地水体指数LSWI时序数据集重采样为统一的时间分辨率,并以一预设阈值对数据集中的陆地水体指数LSWI时序数据进行重新赋值,大于预设阈值的像元赋值为0,小于预设阈值的像元赋值为1,将重新赋值的陆地水体指数LSWI时序数据依次相加求和,获得一个陆地水体指数LSWI重分类数据;S1.2. Using the original satellite images under clear and cloudless conditions for multiple periods during the growing season, the land water index LSWI time series data set is obtained through formula 1. The land water index LSWI time series data set is resampled to a uniform time resolution using a linear interpolation method, and the land water index LSWI time series data in the data set are reassigned with a preset threshold. The pixels greater than the preset threshold are assigned a value of 0, and the pixels less than the preset threshold are assigned a value of 1. The reassigned land water index LSWI time series data are added and summed in sequence to obtain a land water index LSWI reclassification data;

式中:LSWI为陆地水体指数;ρNIR表示近红外波段的反射率;ρSWIR表示短波红外波段的反射率。Where: LSWI is the land water index; ρ NIR represents the reflectivity of the near-infrared band; ρ SWIR represents the reflectivity of the short-wave infrared band.

所述预设阈值为0.2。The preset threshold is 0.2.

S1.3、从Google Earth中选取土地利用样本集作为训练数据,利用支持向量机SVM监督分类算法对步骤S1.1得到的分类识别波段及纹理值和步骤S1.2获得的陆地水体指数LSWI重分类数据进行训练学习,计算得到农业区及非农业区的土地利用分类结果;S1.3, select a land use sample set from Google Earth as training data, use the support vector machine (SVM) supervised classification algorithm to train and learn the classification identification bands and texture values obtained in step S1.1 and the LSWI reclassification data obtained in step S1.2, and calculate the land use classification results of agricultural and non-agricultural areas;

所述土地利用分类结果中,农业区为农田,非农业区包括水体、沙丘、居民区和天然地。In the land use classification results, agricultural areas are farmlands, and non-agricultural areas include water bodies, sand dunes, residential areas and natural land.

S2、应用决策树算法对农业区进行进一步种植结构划分,得到最终农业区高精度土地利用数据:S2. Apply the decision tree algorithm to further divide the planting structure of the agricultural area and obtain the final high-precision land use data of the agricultural area:

S2.1、将步骤S1得到的土地利用分类结果转变为栅格数据并对其进行重新赋值,将农业区赋值为1,非农业区赋值为NoData;将重新赋值的栅格数据转变为矢量数据,获得只包含农业区的shp矢量数据;S2.1, converting the land use classification result obtained in step S1 into raster data and re-assigning it, assigning the agricultural area to 1 and the non-agricultural area to NoData; converting the re-assigned raster data into vector data to obtain shp vector data containing only the agricultural area;

S2.2、基于步骤S2.1得到的只包含农业区的shp矢量数据对生育期内多期晴空无云条件下的原始卫星图像进行裁剪,得到只包含农业区的原始卫星图像,并通过公式二计算得到归一化植被指数NDVI时序数据集,采用线性插值的方法将归一化植被指数NDVI时序数据集重采样为统一的时间分辨率;S2.2, based on the shp vector data containing only the agricultural area obtained in step S2.1, the original satellite images under clear and cloudless conditions for multiple periods during the growth period are cropped to obtain the original satellite images containing only the agricultural area, and the normalized vegetation index NDVI time series data set is calculated by formula 2, and the normalized vegetation index NDVI time series data set is resampled to a uniform time resolution by using a linear interpolation method;

式中:NDVI为归一化植被指数;ρNIR表示近红外波段的反射率;ρRed表示红光波段的反射率。Wherein: NDVI is the normalized difference vegetation index; ρ NIR represents the reflectance of the near infrared band; ρ Red represents the reflectance of the red light band.

S2.3、利用Savitzky-Golay(S-G)滤波器对步骤S2.2重采样后的归一化植被指数NDVI时序数据集进行平滑处理;基于平滑后的NDVI生育期过程曲线建立物候指标判断标准,对农业区每一个像元进行判断,最终得到物候指标栅格数据;所述物候指标栅格数据中包含生育期起始时间、生育期终止时间和生育期长度三个物候指标;S2.3, using a Savitzky-Golay (S-G) filter to smooth the normalized vegetation index NDVI time series data set resampled in step S2.2; establishing a phenological index judgment standard based on the smoothed NDVI growth period process curve, judging each pixel in the agricultural area, and finally obtaining phenological index raster data; the phenological index raster data includes three phenological indicators: the start time of the growth period, the end time of the growth period, and the length of the growth period;

所述物候指标判断标准的建立过程如下:The process of establishing the phenological index judgment standard is as follows:

确定在平滑后的NDVI生育期过程曲线的上升阶段、下降阶段以及NDVI设定值,当NDVI=设定值时,上升阶段所对应的时刻为生育期起始时间,下降阶段所对应的时刻为生育期终止时间,生育期起始时间与生育期终止时间内的时间长度为生育期长度。Determine the rising stage, falling stage and NDVI set value of the smoothed NDVI growing period process curve. When NDVI = set value, the moment corresponding to the rising stage is the starting time of the growing period, and the moment corresponding to the falling stage is the ending time of the growing period. The length of time between the starting time and the ending time of the growing period is the length of the growing period.

所述设定值为0.3。The set value is 0.3.

S2.4、根据不同作物的三个物候指标的值域范围,建立作物分类规则;基于作物分类规则,利用决策树算法对步骤S2.3获得的物候指标栅格数据中的每一个栅格进行判断,获得农业种植结构分类结果,最终使得每个栅格都被划分为某一类具体的作物类型,从而实现对农业区的作物识别。S2.4. Establish crop classification rules according to the value range of the three phenological indicators of different crops; based on the crop classification rules, use the decision tree algorithm to judge each grid in the phenological indicator raster data obtained in step S2.3 to obtain the agricultural planting structure classification results, and finally each grid is divided into a specific crop type, thereby realizing crop identification in agricultural areas.

所述步骤S2得到的农业种植结构分类结果包括小麦、玉米、葵花和果蔬。The agricultural planting structure classification results obtained in step S2 include wheat, corn, sunflower, and fruits and vegetables.

不同作物的三个物候指标的值域范围基于当地实际经验和实地调研分别确定。The value ranges of the three phenological indices for different crops were determined based on local practical experience and field surveys.

S3、将步骤S1得到的土地利用分类结果中非农业区与步骤S2获得的农业种植结构分类结果镶嵌到一起,得到一张带有分类标签色彩的农业种植区土地利用及种植结构分类图。S3. Inlay the non-agricultural areas in the land use classification result obtained in step S1 with the agricultural planting structure classification result obtained in step S2 to obtain a land use and planting structure classification map of the agricultural planting area with classification label colors.

所述农业种植区土地利用及种植结构分类图中的每一个像元都有一个分类,不同的值和颜色代表着不同的分类。Each pixel in the agricultural planting area land use and planting structure classification map has a classification, and different values and colors represent different classifications.

实施例Example

S1、利用支持向量机(SVM)监督学习算法对原始卫星图像进行农业区及非农业区识别;S1. Use the support vector machine (SVM) supervised learning algorithm to identify agricultural and non-agricultural areas in the original satellite images;

将Sentinel-2原始卫星图像每个波段采用双线性插值法重采样为统一的10m空间分辨率,如图2所示;选择一张八月初作物生长旺盛的原始卫星图像,选取该图像中能较好分辨地物的可见光(红绿蓝)波段,以及能判断植物生长状况的红边波段、近红外波段以及短波红外波段作为分类识别波段。利用二阶概率统计方法用一个灰色调空间相关性矩阵,基于3×3的窗口和64灰度量化级别对最优波段的纹理值进行计算,最终得到基于八个纹理滤波(均值、方差、协同性、对比度、相异性、信息熵、二阶矩和相关性)依次计算得到的纹理值,如图3所示;Each band of the Sentinel-2 original satellite image was resampled to a uniform spatial resolution of 10m using bilinear interpolation, as shown in Figure 2. An original satellite image of crops growing vigorously in early August was selected, and the visible light (red, green, and blue) bands that can better distinguish the ground objects in the image, as well as the red edge band, near infrared band, and short-wave infrared band that can judge the growth status of plants were selected as classification and identification bands. The texture value of the optimal band was calculated based on a 3×3 window and 64 grayscale quantization levels using a second-order probability statistical method using a gray tone spatial correlation matrix, and finally the texture value calculated in sequence based on eight texture filters (mean, variance, synergy, contrast, dissimilarity, information entropy, second-order moment, and correlation) was obtained, as shown in Figure 3.

利用生育期内多期晴空无云条件下的Sentinel-2原始卫星图像,经过公式一计算得到陆地水体指数LSWI时序数据集,采用线性插值的方法将陆地水体指数LSWI数据集重采样为统一的5天时间分辨率,并以0.2为阈值对其进行重新赋值,大于0.2的像元赋值为0,小于0.2的像元赋值为1,将重新赋值的陆地水体指数LSWI时序数据依次相加求和,获得一个陆地水体指数LSWI重分类数据;Using Sentinel-2 original satellite images under clear and cloudless conditions during the growing season, the LSWI time series data set of the land water index was calculated by formula 1. The LSWI data set of the land water index was resampled to a uniform 5-day time resolution using the linear interpolation method, and re-assigned with a threshold of 0.2. Pixels greater than 0.2 were assigned a value of 0, and pixels less than 0.2 were assigned a value of 1. The re-assigned LSWI time series data of the land water index were added and summed in turn to obtain a LSWI reclassification data of the land water index.

从Google Earth中人工选取土地利用样本集(居民地、沙丘、荒地、水体和农田)作为训练数据,利用支持向量机SVM监督分类算法对最优波段及纹理值以及LSWI重分类数据进行训练学习,计算得到农业区及非农业区土地利用分类结果,如图4所示。A land use sample set (residential area, sand dune, wasteland, water body and farmland) was manually selected from Google Earth as training data. The support vector machine (SVM) supervised classification algorithm was used to train and learn the optimal band and texture value as well as the LSWI reclassification data. The classification results of land use in agricultural and non-agricultural areas were calculated, as shown in Figure 4.

S2、应用决策树算法对农业区进行进一步种植结构划分,得到最终农业区高精度土地利用数据;S2, applying the decision tree algorithm to further divide the planting structure of the agricultural area and obtain the final high-precision land use data of the agricultural area;

将土地利用分类结果转变为栅格数据并对其进行重新赋值,将农业区赋值为1,非农业区统一赋值为NoData,将重新赋值的栅格数据转变为矢量数据,得到农业区的shp矢量数据;The land use classification results are converted into raster data and re-assigned, with the agricultural area assigned to 1 and the non-agricultural area uniformly assigned to NoData. The re-assigned raster data are converted into vector data to obtain the shp vector data of the agricultural area.

基于农业区矢量数据对生育期内多期晴空无云条件下的原始卫星图像进行裁剪,得到只包含农业区的原始卫星图像,通过公式二计算得到归一化植被指数NDVI时序数据集,将时序数据集采用线性插值的方法重采样为统一的5天时间分辨率;Based on the vector data of agricultural areas, the original satellite images under clear and cloudless conditions during the growth period were cropped to obtain the original satellite images containing only the agricultural areas. The normalized difference vegetation index NDVI time series data set was calculated by formula 2, and the time series data set was resampled to a unified 5-day time resolution using the linear interpolation method.

利用Savitzky-Golay(S-G)滤波器对生育期NDVI时序数据集进行平滑处理。如图5所示,基于平滑后的NDVI生育期过程曲线,确定在曲线的上升阶段以及下降阶段,当NDVI=0.3时,上升阶段以及下降阶段所对应的时刻分别为生育期起始时间及生育期终止时间,生育期起始时间与生育期终止时间内的时间长度为生育期长度,基于上述物候指标判断标准对农业区每一个像元进行判断,最终得到包含生育期起始时间、生育期终止时间以及生育期长度三个物候指标的物候指标栅格数据,用于进一步的作物分类。The Savitzky-Golay (S-G) filter was used to smooth the NDVI time series data set during the growth period. As shown in Figure 5, based on the smoothed NDVI growth period process curve, the rising and falling stages of the curve were determined. When NDVI = 0.3, the rising and falling stages corresponded to the start time and end time of the growth period, respectively. The length of time between the start time and the end time of the growth period was the length of the growth period. Based on the above phenological index judgment standard, each pixel in the agricultural area was judged, and finally the phenological index grid data containing the three phenological indicators of the start time, end time and length of the growth period were obtained for further crop classification.

基于当地实际经验和实地调研,分别确定不同作物的三个物候指标的值域范围,建立作物分类规则,如图6所示。基于分类规则,利用决策树算法对物候指标栅格数据中的每一个栅格进行判断,最终使得每个栅格都被划分为某一类具体的作物类型,从而实现对农业区的作物识别,得到如图7所示的农业区种植结构分类结果。Based on local actual experience and field research, the value ranges of the three phenological indicators of different crops were determined and crop classification rules were established, as shown in Figure 6. Based on the classification rules, the decision tree algorithm was used to judge each grid in the phenological indicator grid data, and finally each grid was divided into a specific crop type, thereby realizing crop identification in agricultural areas and obtaining the classification results of agricultural area planting structure as shown in Figure 7.

将土地利用结果中的非农业区与种植结构识别结果镶嵌到一起,如图8所示,进而得到农业区高精度土地利用分类结果,分类的结果是得到带有分类标签色彩的一张图像,包括水体、沙丘、居民区、天然地非农业区土地利用类型以及小麦、玉米、葵花和果蔬种植结构分类结果,各类地物及作物的图例如图9所示。The non-agricultural areas in the land use results are inlaid with the planting structure identification results, as shown in Figure 8, and then the high-precision land use classification results of the agricultural area are obtained. The classification result is an image with classification label colors, including water bodies, sand dunes, residential areas, natural non-agricultural land use types, and wheat, corn, sunflower and fruit and vegetable planting structure classification results. Examples of various land features and crops are shown in Figure 9.

Claims (7)

1. The remote sensing identification method for the ground cover and planting structure of the finely divided agricultural area is characterized by comprising the following steps:
S1, carrying out agricultural region and non-agricultural region identification on an original satellite image by using a support vector machine SVM supervised learning algorithm:
S1.1, resampling each wave band of a plurality of original satellite images with different meteorological conditions in different periods into uniform spatial resolution by adopting a bilinear interpolation method; selecting an original satellite image with vigorous growth of the early-stage August crops, and selecting a visible light wave band capable of distinguishing ground features and a red-edge wave band, a near-infrared wave band or a short-wave infrared wave band capable of judging plant growth conditions in the image as classification recognition wave bands; calculating texture values of the classification and identification bands based on a 3×3 window and 64 gray quantization levels by using a gray tone space correlation matrix by using a second-order probability statistical method;
S1.2, obtaining a land water index LSWI time sequence data set by utilizing an original satellite image under a cloudless condition of a multi-period clear sky in a growth period, resampling the land water index LSWI time sequence data set into uniform time resolution by adopting a linear interpolation method, reassigning the land water index LSWI time sequence data in the data set by using a preset threshold value, assigning pixels larger than the preset threshold value to 0, assigning pixels smaller than the preset threshold value to 1, sequentially adding and summing the reassigned land water index LSWI time sequence data to obtain land water index LSWI reclassification data;
wherein: LSWI is land water index; ρ NIR represents the reflectivity in the near infrared band; ρ SWIR represents the reflectivity of the short wave infrared band;
S1.3, selecting a land utilization sample set from Google Earth as training data, utilizing a support vector machine SVM supervised classification algorithm to train and learn the classification recognition wave band and texture value obtained in the step S1.1 and land water index LSWI reclassification data obtained in the step S1.2, and calculating to obtain land utilization classification results of agricultural areas and non-agricultural areas;
S2, carrying out further planting structure division on the agricultural area by applying a decision tree algorithm to obtain final agricultural area high-precision land utilization data:
S2.1, converting land utilization classification results obtained in the step S1 into raster data, reassigning the raster data, assigning an agricultural area to 1, and assigning a non-agricultural area to NoData; converting the reassigned raster data into vector data to obtain shp vector data only containing an agricultural area;
S2.2, cutting an original satellite image under a multi-period clear sky cloudless condition in a growing period based on shp vector data only comprising an agricultural area obtained in the step S2.1 to obtain an original satellite image only comprising the agricultural area, calculating through a formula II to obtain a normalized vegetation index NDVI time sequence data set, and resampling the normalized vegetation index NDVI time sequence data set to uniform time resolution by adopting a linear interpolation method;
Wherein: NDVI is normalized vegetation index; ρ NIR represents the reflectivity in the near infrared band; ρ Red represents the reflectivity of the red band;
S2.3, smoothing the normalized vegetation index NDVI time sequence data set resampled in the step S2.2 by utilizing a Savitzky-Golay filter; establishing a weather indicator judgment standard based on the smoothed NDVI growth period process curve, judging each pixel of the agricultural area, and finally obtaining weather indicator grid data; the weather indicator grid data comprises three weather indicators including a growth period starting time, a growth period ending time and a growth period length;
The establishment process of the weather indicator judgment standard is as follows:
Determining an ascending phase, a descending phase and an NDVI set value of the smoothed NDVI fertility phase process curve, wherein when NDVI=set value, the moment corresponding to the ascending phase is a fertility phase starting time, the moment corresponding to the descending phase is a fertility phase ending time, and the time length in the fertility phase starting time and the fertility phase ending time is a fertility phase length;
S2.4, establishing crop classification rules according to the value range of three weather indexes of different crops; based on crop classification rules, judging each grid in the weather indicator grid data obtained in the step S2.3 by utilizing a decision tree algorithm to obtain an agricultural planting structure classification result, and finally dividing each grid into a certain specific crop type, thereby realizing crop identification of an agricultural area;
and S3, inlaying the non-agricultural area in the land utilization classification result obtained in the step S1 and the agricultural planting structure classification result obtained in the step S2 together to obtain a land utilization and planting structure classification diagram of the agricultural planting area with classification label colors.
2. The method for remotely sensing and identifying land cover and planting structures in finely divided agricultural areas according to claim 1, wherein in the step S1.2, the preset threshold is 0.2.
3. The method for remotely identifying land cover and planting structures in finely divided agricultural areas according to claim 1, wherein in the step S1.3, the agricultural area is a farmland, and the non-agricultural area includes water, sand dunes, residential areas and natural lands.
4. The method for remotely sensing and identifying land cover and planting structures in finely divided agricultural areas according to claim 1, wherein in the step S2.3, the set value is 0.3.
5. The method for remotely identifying land cover and planting structures in finely divided agricultural areas according to claim 1, wherein the classification result of agricultural planting structures obtained in step S2.4 comprises wheat, corn, sunflower and fruits and vegetables.
6. The method for remotely sensing and identifying the ground cover and planting structure of a finely divided agricultural area according to claim 1, wherein in the step S2.4, the value ranges of three physical indexes of different crops are respectively determined based on local actual experience and field investigation.
7. The method for remote sensing identification of finely divided agricultural land cover and planting structures according to claim 1, wherein in said step S3, each pixel in the classification map of agricultural land utilization and planting structures has a classification, and different values and colors represent different classifications.
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