| 面向卫星遥感影像检索定位的深度学习全局表征模型评估与分析 |
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| 引用本文： | 施群山,蓝朝桢,徐青,周杨,胡校飞.面向卫星遥感影像检索定位的深度学习全局表征模型评估与分析[J].地球信息科学,2022,24(11):2245-2263. |
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| 作者姓名： | 施群山 蓝朝桢 徐青 周杨 胡校飞 |
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| 作者单位： | 战略支援部队信息工程大学地理空间信息学院，郑州 450001 |
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| 基金项目： | 国家自然科学基金项目(41701463) |
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| 摘 要： | 如何快速获取无辅助参数卫星遥感影像地理位置是非合作方式获取的遥感影像信息充分利用的一个关键，利用影像特征的相似性对卫星遥感影像检索来实现定位，是获取无辅助参数卫星遥感影像地理位置的有效手段。为了探寻影像深度学习全局特征用于无辅助参数卫星遥感影像检索定位的可行性，建立了包括Precision@K、平均排序、特征提取时间、特征相似性计算时间、硬件消耗等，涵盖有效性、效率2个方面共计5类指标的评估体系。采用谷歌地球提供的影像数据作为基准影像，在资源三号夏季及冬季数据集上，分别利用AlexNet、VggNet、ResNet、DenseNet、EfficientNet等几种代表性的卷积神经网络预训练模型提取基准影像及查询影像的全局特征，依据评估体系中的指标，对这些网络模型的影像表征效果进行全面的量化评估与分析。试验分析结果表明，DenseNet、ResNet-18、VggNet这3个深度学习神经网络预训练模型提取的全局特征，综合表征效果较好，可有效用于卫星遥感影像检索定位；当K值取200时，DenseNet网络模型的Precision@K值可以达到59.5%，ResNet-18和VggNet网络模型紧随其后，分别为49.7%和48.0%，为进一步利用深度学习全局特征进行卫星遥感影像检索定位，找出了最佳的候选网络模型，为下一步模型优化等研究奠定了基础。
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| 关 键 词： | 卫星遥感影像 检索定位 深度学习 卷积神经网络 全局特征 图像表征 有效性评估 效率评估 |
| 收稿时间： | 2022-04-03 |
Evaluation and Analysis of Deep Learning Global Representation Model for Satellite Remote Sensing Image Retrieval and Location |
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| SHI Qunshan,LAN Chaozhen,XU Qing,ZHOU Yang,HU Xiaofei.Evaluation and Analysis of Deep Learning Global Representation Model for Satellite Remote Sensing Image Retrieval and Location[J].Geo-information Science,2022,24(11):2245-2263. |
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| Authors: | SHI Qunshan LAN Chaozhen XU Qing ZHOU Yang HU Xiaofei |
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| Institution: | Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China |
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| Abstract: | How to quickly obtain the geographical location of satellite remote sensing images without auxiliary parameters is a key to make full use of remote sensing image information obtained by non-cooperative means. Using the similarity of image features to realize satellite remote sensing image retrieval is an effective means to obtain the geographical location of satellite remote sensing images without auxiliary parameters. In order to explore the feasibility of deep learning derived global features for satellite remote sensing image retrieval and positioning without auxiliary parameters, an evaluation system considering both effectiveness and efficiency is established, which quantifies the Precision@K, average ranking, feature extraction time, feature similarity calculation time, and hardware consumption. Using the image data provided by Google Earth as the reference image, the summer and winter data from ZY-3 as the test datasets, several representative convolution neural network such as AlexNet, VggNet, ResNet, DenseNet, and EfficientNet are trained and used to extract the global features of the reference image and test datasets, respectively. Using multiple indicators of the evaluation system, the image representation capability of these models is comprehensively evaluated and quantitatively analyzed. The results show that: (1) the global features extracted by deep learning models have higher effectiveness in satellite remote sensing image retrieval and positioning. Compared with local features, these models provide a new way for satellite remote sensing image retrieval and positioning; (2) based on the test datasets, the performance of DenseNet, ResNet-18, and VggNet is relatively better, and the precision@K of DenseNet is the highest, indicating the highest success rate. The success rate is also a primary index in satellite remote sensing image retrieval and positioning. The mAR of ResNet-18 is close to that of VggNet and slightly higher than that of DenseNet model. In terms of efficiency, the ResNet-18 model is better among the three models, with less feature extraction time, the least feature similarity calculation time, and the smallest feature file. Its feature vector has only 512 dimensions, but its effectiveness is close to the DenseNet model; (3) The deep learning derived global features have good robustness using different image resolutions. With different resolutions, the corresponding cosine distance and the sorting number of the correct image change little in this study, which can overcome the limitation in existing satellite remote sensing image retrieval and positioning methods with different resolutions; (4) Among these models, the feature extraction of AlexNet takes the least time, and EfficientNet_b7 takes the most time in feature extraction. The image size, feature element type, and texture richness have little impact on the time of feature extraction; (5) For the image representation with poor texture information such as desert, ocean, cloud, and continuous mountain, the representation ability of deep learning models needs to be further improved. |
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| Keywords: | satellite remote sensing image retrieval and location deep learning convolutional neural network global features image representation effectiveness efficiency |
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