摘要
由于遥感场景图像类内差距大即同一类别图像的特性信息相差较大,仅仅依靠特性信息分类的准确率不高,而现有遥感场景图像分类方法忽视了同一类别所具有的相同的共性信息也可以辅助图像识别,对此本文提出一种基于共性与特性信息融合的遥感场景图像分类方法。首先,图像通过卷积网络较浅层与深层得到的简单特征图与复杂特征图相叠加,可认为是此图像注意力集中的特征图,提取此特征图的手工特征LBP作为共性信息。之后与卷积网络提取的特性信息融合并进行分类。本文使用经贝叶斯优化优化超参数的SVM分类器,使其性能达到最佳来消除分类器对实验的影响。在两个数据集UC Merced和AID上的实验,验证其分类精度分别达到了98.80%和96.06%,表明该方法能有效地提升遥感场景图像准确率。在国防,城市规划,地质勘查等领域有重要意义。
Due to the large intra-class gap of remote sensing scene images, that is, the feature information of the same category of images is quite different, the accuracy of classification based only on feature information is not high, and the existing remote sensing scene image classification methods ignore the same common information of the same category. It can assist in image recognition. This paper proposes a remote sensing scene image classification method based on the fusion of common and characteristic information. First, the simple feature map and the complex feature map obtained by the shallower and deeper layers of the convolutional network are superimposed on the image, which can be considered as the feature map with concentrated attention of the image, and the handcrafted feature LBP of this feature map is extracted as the common information. It is then fused with the feature information extracted by the convolutional network and classified. In this paper, the SVM classifier whose hyperparameters are optimized by Bayesian optimization is used to achieve the best performance to eliminate the influence of the classifier on the experiment. Experiments on the two datasets UC Merced and AID verify that the classification accuracy reaches 98.80% and 96.06%, respectively, indicating that the method can effectively improve the accuracy of remote sensing scene images. It is of great significance in the fields of national defense, urban planning, and geological exploration.
作者
高翔
王李祺
魏志晴
白艳萍
Gao Xiang;Wang Liqi;Wei Zhiqing;Bai Yanping(School of Science,North University of China,Taiyuan 030051,China)
出处
《电子测量技术》
北大核心
2022年第21期104-110,共7页
Electronic Measurement Technology
基金
国家自然科学基金(61774137,51875535 and 61927807)
山西省重点研发计划项目(201903D121156)
山西省回国留学人员科研项目(2020-104,2021-108)资助。
关键词
图像分类
SVM
特征融合
特征工程
image classification
SVM
feature fusion
feature representation
