国家自然科学基金青年基金(41701491); 福建省自然科学基金面上项目(2017J01464, 2018J01619)
城市遥感图像目标识别能够监测城市地物类型, 是近年来的热点研究话题, 然而, 基于像元的传统方法不能充分利用高分辨率遥感图像的特征信息, 基于对象的传统方法无法精确提取到对象. 针对传统方法的不足, 本文提出一种基于多特征空间及其优化的城市遥感图像目标识别方法, 该方法以两种传统方法为前提, 在联合像元特征与对象特征的基础上, 补充VGG19网络提供的深度特征来构建多特征空间, 利用XGBoost算法对多特征空间进行特征选择, 建立了一个最优特征空间, 最后送入随机森林识别器, 从而实现对城市遥感图像目标的识别. 实验结果显示, 本文方法的识别精度达到87.89%, Kappa系数达到0.83, 对研究区域具有较高的识别能力, 是一种城市遥感图像目标识别的有效方法.
Target recognition in urban remote sensing images can help monitor the types of urban features and is a hot research topic in recent years. However, the traditional pixel-based method cannot make full use of the features of high-resolution remote sensing images, whereas the traditional object-based method cannot accurately extract the objects. To address the shortcomings of the traditional methods, this study proposes a method of target recognition in urban remote sensing images based on the multi-feature space and its optimization. This method takes the two traditional methods as the premise, combines pixel features with object features, and constructs the multi-feature space by supplementing depth features provided by the VGG19 network. The XGBoost algorithm is used to select features in the multi-feature space. An optimal feature space is established and sent to the random forest recognizer to achieve the target recognition in urban remote sensing images. The experimental results show that the recognition accuracy of the proposed method is 87.89%, and the Kappa coefficient is 0.83, which means this method displays a high recognition capability in the study area and is an effective method for target recognition in urban remote sensing images.