GDAL插值使用示例
2024-04-11 20:40:11
之前写的博客《GDAL源码剖析（十三）之GDAL网格插值说明》里面大致说明了GDAL插值的一些方法和原理，今天使用一部分示例数据进行说明。
首先准备插值用的点数据，这里使用的数据大概是126个点组成的，排列按照X、Y、Z顺序。内容如下：
53414.28,31421.88,39.555
53387.8,31425.02,36.8774
53359.06,31426.62,31.225
53348.04,31425.53,27.416
53344.57,31440.31,27.7945
53352.89,31454.84,28.4999
53402.88,31442.45,37.951
53393.47,31393.86,32.5395
53358.85,31387.57,29.426
53358.59,31376.62,29.223
53348.66,31364.21,28.2538
53362.8,31340.89,26.8212
53335.73,31347.62,26.2299
53331.84,31362.69,26.6612
53351.82,31402.35,28.4848
53335.09,31399.61,26.6922
53331.15,31333.34,24.6894
53344.1,31322.26,24.3684
53326.8,31381.66,26.7581
53396.59,31331.42,28.7137
53372.7,31317.25,25.8215
53404.54,31313.74,26.9055
53416.04,31349.16,31.7509
53424.7,31367.77,34.8919
53414.85,31383.5,37.4818
53399.59,31370.21,32.5866
53386.89,31353.32,30.5459
53383.23,31336.82,29.2504
53421.13,31322.59,27.7593
53468.29,31316.53,27.0276
53434.93,31313.32,26.5662
53456.71,31324.05,28.8742
53491.54,31372.23,33.0459
53470.83,31363.75,32.6194
53414.07,31397.87,39.5041
53446.84,31368.77,34.5865
53438,31337.25,30.1398
53456.08,31344.36,30.1871
53472.76,31401.02,38.5963
53479.13,31432.82,42.3405
53499.63,31422.7,40.7577
53492.35,31402.93,37.9286
53489.32,31390.16,36.34
53449.7,31383.25,36.9367
53444.56,31406.78,41.6945
53464.5,31427.23,43.5075
53503.60,31439.93,41.0365
53515.85,31437.89,39.9929
53505.89,31493.01,33.8673
53485.63,31490.18,36.4479
53493.83,31472.49,39.8801
53482.52,31450.67,42.3327
53508.10,31461.98,40.2172
53513.7,31482.32,36.919
53532.14,31466.79,37.7726
53547.81,31446.42,38.3385
53555.09,31429.29,37.5484
53550.59,31466.8,37.233
53542.39,31490.08,33.8351
53562.32,31481.02,34.4659
53582.49,31461.23,34.1418
53585.43,31474.09,32.5303
53596.28,31464.84,31.7542
53573.76,31494.1,31.3335
53567.67,31505.61,29.5076
53435.85,31465.51,40.6101
53446.92,31438.86,43.5085
53451.21,31462.16,42.1787
53425.14,31442.84,42.2289
53465.83,31471.64,41.4353
53444.54,31478.39,39.4434
53436.92,31489.73,35.15
53446.87,31502.75,32.0204
53424.8,31499.59,31.1902
53415.55,31477.23,36.1728
53405.41,31459.37,36.2749
53407.98,31487.67,31.2808
53410.21,31511.39,29.09
53392.51,31495.92,29.1024
53434.96,31524.73,28.8913
53415.92,31523.57,28.6408
53381.47,31504.81,28.3432
53365.02,31495.85,27.6838
53352.99,31500.58,26.1047
53348.82,31486.82,26.2623
53350.47,31471.08,27.5493
53357.64,31521.57,25.8516
53387.14,31526.14,25.9322
53418.4,31538.09,25.2847
53448.49,31533.77,25.8802
53465.16,31494.46,34.603
53458.79,31508.1,31.0874
53503.63,31519.64,28.9581
53504.38,31505.74,29.4945
53487.85,31513.90,29.3574
53473.73,31522.38,28.2401
53474.65,31534.46,28.1753
53501.33,31537.72,27.601
53526.89,31530.08,29.1993
53538.63,31519.63,29.2767
53327.38,31431.95,26.6129
53326.22,31419.39,26.2965
53591.41,31400.14,31.3133
53580.18,31375.51,30.2236
53603.18,31381.36,28.8233
53529.15,31341.71,27.0601
53583.86,31408.9,33.6867
53565.07,31419.84,36.2957
53544.34,31402.71,36.3476
53568.31,31403.82,34.7975
53517.51,31388.7,35.1603
53521.11,31366.48,31.672
53539.64,31349.28,29.138
53506.62,31335.71,26.9185
53505.15,31350.97,29.6483
53494.79,31379.74,33.5259
53511.44,31374.64,33.1928
53493.14,31354.75,30.8649
53549.66,31508.67,30.3146
53369.18,31500.28,27.7
53442.8,31431.06,43.90
53455.78,31444.08,43.460
53461.02,31374.57,35.5
53437.30,31388.9,38.2
53399.61,31407.54,37
53514.12,31450.64,40.1
下面就开始读取坐标点数据进行插值处理，大致流程是：
首先读取点坐标存入数组中，并统计点的最大最小值，用于计算输出图像的大小；
其次是构造插值算法的参数，并调用GDAL插值函数进行插值；
最后将插值的结果写入图像，释放资源等。
代码如下：
void GDALGridTest()
{const char* filename = "E:\\Datum\\GDALGrid\\离散点数据文件.dat";const char* outputfullname = "E:\\Datum\\GDALGrid\\插值结果.tif";//计算最大最小值double dfXMin;double dfXMax;double dfYMin;double dfYMax;double dfZMin;double dfZMax;GUInt32 nPoints = 126;double * padfX = new double[nPoints]; double * padfY = new double[nPoints]; double * padfZ = new double[nPoints]; //将文本文件读入数组并统计出最大最小值SET_LOCAL;ifstream ifile(filename, ios::in);string strBuf;int i = 0;while(getline(ifile, strBuf)){vector<string> vStr;// 使用boost库的split拆分字符串split(vStr, strBuf, is_any_of(","), token_compress_on);double tmpX = atof(vStr[0].c_str());double tmpY = atof(vStr[1].c_str());double tmpZ = atof(vStr[2].c_str());padfX[i] = tmpX;padfY[i] = tmpY;padfZ[i] = tmpZ;if(i = = 0){dfXMin = tmpX;dfXMax = tmpX;dfYMin = tmpY;dfYMax = tmpY;dfZMin = tmpZ;dfZMax = tmpZ;}dfXMin = (tmpX<dfXMin ) ? tmpX : dfXMin;dfXMax = (tmpX>dfXMax ) ? tmpX : dfXMax;dfYMin = (tmpY<dfYMin ) ? tmpY : dfYMin;dfYMax = (tmpY>dfYMax ) ? tmpY : dfYMax;dfZMin = (tmpZ<dfZMin ) ? tmpZ : dfZMin;dfZMax = (tmpZ>dfZMax ) ? tmpZ : dfZMax;i++;}ifile.close();REVERT_LOCAL;//计算图像大小double pixResoultion = 0.5; //设置分辨率为0.5GUInt32 nXSize = (dfXMax-dfXMin) / pixResoultion;GUInt32 nYSize = (dfYMax-dfYMin) / pixResoultion;GDALAllRegister();// 离散点内插方法，使用反距离权重插值法GDALGridInverseDistanceToAPowerOptions *poOptions = new GDALGridInverseDistanceToAPowerOptions();poOptions->dfPower = 2;poOptions->dfRadius1 = 20;poOptions->dfRadius2 = 15;char *pData = new char[nXSize*nYSize];// 离散点内插方法，使用反距离权重插值法。使用其他的插值算法，这里换成其他的，还有下面的GDALGridCreate函数的对应参数GDALGridCreate(GGA_InverseDistanceToAPower, poOptions, nPoints, padfX, padfY, padfZ, dfXMin, dfXMax, dfYMin, dfYMax, nXSize, nYSize, GDT_Byte, pData, NULL, NULL);// 创建输出数据集，格式为GeoTiff格式GDALDriver * pDriver = NULL;pDriver = GetGDALDriverManager()->GetDriverByName("Gtiff");GDALDataset *poDataset = pDriver->Create(outputfullname, nXSize,nYSize, 1, GDT_Byte, NULL);// 设置六参数double adfGeoTransform[6] = {dfXMin, pixResoultion, 0 , dfYMax, 0, -pixResoultion};poDataset->SetGeoTransform(adfGeoTransform );// 写入影像poDataset->RasterIO(GF_Write, 0, 0, nXSize, nYSize, pData, nXSize, nYSize, GDT_Byte, 1, 0, 0, 0, 0);// 释放资源 关闭图像delete poOptions;delete []pData;GDALClose(poDataset); poDataset = NULL;
}顺便说一下，上面代码中有两个宏定义代码，其作用是使用防止ifstream打开中文路径的文件错误，具体的定义如下：
/**
* @brief 将全局区域设为操作系统默认区域
*/
#define SET_LOCAL { locale::global(locale("")); setlocale(LC_ALL,"Chinese-simplified"); }
/**
* @brief 还原全局区域设定
*/
#define REVERT_LOCAL locale::global(locale("C"))
最 后放一张插值后的图片：
原始图像
使用ArcMap渲染后的图像
GDAL插值使用示例相关推荐
- GDAL工具使用示例（一）
GDAL提供的工具都是非常的实用,后续打算写一系列的博文来对GDAL提供的工具进行简单的说明,并结合实际的例子进行演示,希望对数据处理的同学有用. 首先说明一下需要准备的数据,我这里使用的是SRTM的 ...
- java实现gdal栅格矢量化_gdal栅格矢量化 - osc_lfs4vsih的个人空间 - OSCHINA - 中文开源技术交流社区...
#include "gdal_alg.h" 栅格矢量化功能用于将栅格数据生成矢量数据,通常用于分类图像.GDAL库中使用函数GDALPolygonize()或者函数GDALFPol ...
- 【Android 属性动画】属性动画 Property Animation 工作原理 ( 线性插值动画 | 非线性插值动画 | 动画计算 | 经过分数 | 插值分数 | 类型估值器)
文章目录 一.线性插值动画示例 二.非线性插值动画示例 三.动画计算方式 四.时间经过分数 ( Elapsed Fraction ) 五.插值分数 ( Interpolated Fraction ) ...
- 线性插值插值_揭秘插值搜索
线性插值插值 搜索算法指南 (Searching Algorithm Guide) Prior to this article, I have written about Binary Search. ...
- （6）vue.js基础语法—插值表达式
一.vscode插件介绍 在我们演示插值表达式之前,我们先安装这一个VScode给我们提供的插件,它可以将我们书写好的网页通过服务端口的方式进行访问,同时它的好处还有:再修改代码后,不需要我们手动点击 ...
- swift实例教程_Swift示例教程基础
swift实例教程 In this tutorial we'll look into the basic syntax of Swift and run it in our console. 在本教程 ...
- sinc插值原理及其实现
前言 数字信号和图像处理中经常用到的样本位置的移动主要是通过插值实现的.根据采样定理,在满足 1)信号是带限的,即最高频率有界: 2)满足奈奎斯特采样率,即实信号的采样率高于最高频率的两倍.复信号采样 ...
- 数学建模——插值与拟合
文章目录 一.插值和拟合 1.什么是插值与拟合? 2.常用一维插值方法 (1)分段线性插值 (2)样条插值 3.常用二维插值方法 (1)插值节点为网格数据 (2)插值节点为非网格数据 4.拟合 二.插 ...
- 光脚丫思考Vue3与实战：第04章 模板语法 第01节 概述+插值
下面是本文的屏幕录像的在线视频: 光脚丫思考Vue3与实战:第04章 模板语法 第01节 插值 温馨提示: 1.视频下载:线上视频被压缩处理,可以下载高清版本: 链接:https://pan.baid ...
最新文章
- python使用matplotlib可视化堆叠的直方图（stacked histogram plot）、多个类别的数据在直方图区间层面累积堆叠起来
- python 3.5(socket)
- 软件设计师-软件知识产权保护
- 对于防止按钮重复点击的尝试
- 如何将Git用于Unity3D源代码管理？
- 我国低轨宽带通信卫星系统建设迈出了实质性的一步
- 'mysql' 不是内部或外部命令，也不是可运行的程序或批处理文件的解决办法
- VJC案例-风扇定时
- Python模块安装：Python3安装pdfminer3k
- 逻辑教育大厂必备IOS面试突击班
- vector注意事项，vector subscript out of range
- 80后发财靠康波：周金涛生前20篇雄文精华，一文尽览
- 【一步步学OpenGL 19】 -《镜面反射光》
- ipynb 文件转 py
- 田园仿古砖特点大汇总，田园风格你选对了吗？
- 裁判文书数据-大数据分析用例
- 计算机实时控制和过程控制,实时控制程序
- Linux学习笔记 16（存储设备管理）
- gulp之ES6转ES5
- Ubuntu系统下使用ROS（moveit ）连接ABB实体机器人（irb120）（1.控制仿真）