| 联合像元-深度-对象特征的遥感图像城市变化检测 |
| |
| 引用本文： | 赵生银,安如,朱美如.联合像元-深度-对象特征的遥感图像城市变化检测[J].测绘学报,2019,48(11):1452-1463. |
| |
| 作者姓名： | 赵生银 安如 朱美如 |
| |
| 作者单位： | 河海大学地球科学与工程学院,江苏南京,211100;河海大学地球科学与工程学院,江苏南京,211100;河海大学地球科学与工程学院,江苏南京,211100 |
| |
| 基金项目： | 江苏省重点研发计划（BE2017115）；国家自然科学基金项目（41871326；41271361）；"十二五"国家科技支撑计划（2013BAC03B04） |
| |
| 摘 要： | 特征空间的构建和优化对遥感图像识别能力的提高具有重要作用。针对面向对象方法对波段光谱信息利用不足,以及像元识别法无法充分利用图像空间几何等信息的问题,本文建立了新颖的联合像素级和对象级特征的航摄遥感图像城市变化检测方法。首先,充分利用像素级和对象级特征的优势,建立考虑光谱、指数、纹理、几何、表面高度及神经网络深度特征的特征空间;然后,引入LightGBM(light gradient boosting machine)算法对大量特征进行选择研究;最后,采用随机森林识别器对宜兴市2012年和2015年两期遥感图像进行识别,利用变化矩阵进行城市的变化检测。结果表明:联合像元、深度、对象特征和LightGBM特征选择算法的识别效果最好,平均的总体识别精度达到了88.50%,Kappa系数达到0.86,比基于像元、深度或对象特征的识别方法分别提高了10.50%、15.00%和4.00%;城市变化检测精度达到了87.50%。因此,本文方法是利用甚高分辨率航摄遥感图像进行城市变化的检测的有效方法。
|
| 关 键 词： | 联合像元-深度-对象特征 卷积神经网络特征 LightGBM算法 特征选择 城市变化检测 航摄遥感图像 |
| 收稿时间： | 2017-11-30 |
| 修稿时间： | 2018-08-26 |
Urban change detection by aerial remote sensing using combining features of pixel-depth-object |
| |
| ZHAO Shengyin,AN Ru,ZHU Meiru.Urban change detection by aerial remote sensing using combining features of pixel-depth-object[J].Acta Geodaetica et Cartographica Sinica,2019,48(11):1452-1463. |
| |
| Authors: | ZHAO Shengyin AN Ru ZHU Meiru |
| |
| Institution: | School of Earth Science and Engineering, Hohai University, Nanjing 211100, China |
| |
| Abstract: | The selection and optimization of features play an important role in the recognition of remote sensing image. Usually, object-based methods cannot make full use of spectral information whereas pixel-based cannot take the advantage of spatial geometry information of remote sensing images. Therefore, a novel urban change detection method is proposed to combine pixel-scale and object-scale features from aerial remote sensing images. First, a feature space was established with spectrum, derivative index, texture, geometry, surface height and convolution neural network layers features. Second, a large number of important features were selected by using LightGBM (Light Gradient Boosting Machine) algorithm. Third, the selected features were used in the random forest classifier to produce the land cover maps from the aerial remote sensing images of Yixing City in 2012 and 2015. Finally, change matrix was used to detect urban change. The results show that the combination of pixel, depth, object features and LightGBM feature selection algorithm has the best recognition effect. Meanwhile, the average accuracy and Kappa coefficient of the proposed method are 88.50% and 0.86, which is 10.50%, 15.00% and 4.00% higher than that based on pixel, deep or object features recognition alone. And the accuracy of urban change detection is 87.50%. Hence, the proposed method is an effective method for urban change detection using aerial remote sensing images. |
| |
| Keywords: | |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《测绘学报》浏览原始摘要信息 |
| 点击此处可从《测绘学报》下载免费的PDF全文 |
|
