一种无人机正射影像地类自动识别方法
A Method for Automatic Land Classification of UAV Digital Orthophoto Map
金振阳 1章迪 2方田野 2石淼3
作者信息
- 1. 广州全成多维信息技术有限公司,广东 广州 511457;武汉大学测绘学院,湖北 武汉 430079
- 2. 武汉大学测绘学院,湖北 武汉 430079
- 3. 武汉市测绘研究院,湖北 武汉 430022
- 折叠
摘要
当前,对无人机正射影像的地类识别仍主要依靠人机交互的方式进行,受作业人员熟练度制约,生产效率较低、生产成本较高.为此,提出了一种无人机正射影像地类自动识别方法,即利用VGG Image Annotator对影像地类按最小单元标注获得精细化样本;搭建以ResNet50为特征提取器的Mask R-CNN网络;基于预训练模型、利用地类样本对网络进行训练和测试.利用某地1m分辨率的无人机数字正射影像制作了房屋、耕地、森林、水域四种地类样本,依托TensorFlow-gpu 1.11.0和Keras2.0.9搭建训练和测试环境,结果表明,四种地类识别的F1值可达70％以上,证明了本方法的可行性.
Abstract
The land classification of UAV digital orthophoto map still mainly depends on human-computer interaction,which requires ex-perienced and qualified operators,with low efficiency and high cost. Therefore,a method for automatic land classification of UAV Digital Or-thophoto Map was proposed,using VGG Image Annotator to divide the land types in the image into minimum units to obtain refined samples;building a Mask R-CNN network with ResNet 50 as the feature extractor;training and testing the network using pre-trained models and land type samples. Sample set was produced using UAV digital orthophoto image of a certain region,with 1m resolution,and a training and testing environment was built based on TensorFlowgpu 1. 11. 0 and Keras 2. 0. 9. The test results show that the F1 value of this method for identifying four land types,including house,plowland,forest and water,can reach over 70％,which demonstrates that the new method was feasible.
关键词
无人机/数字正射影像/地类识别/Mask/R-CNNKey words
UAV/digital orthophoto map/land classification/Mask R-CNN引用本文复制引用
基金项目
湖北省自然科学基金计划项目资助(2022CFB090)
中央高校基本科研业务费专项资金资助(2042023kf0002)
第三次全国土地调查项目(445323-201809-324002-0019)
出版年
2024
NETL
NSTL
万方数据