1背景随着机载测试平台的能力越来越强,架构越来越复杂,机载测试和评估需求也在大幅增长,尤其是高速大量数据采集需求。随着机载测试平台产生的数据大幅增长,当前传统的航空移动遥测(Aeronautical Mobile Telemetry,AMT)通信设备即将遇...1背景随着机载测试平台的能力越来越强,架构越来越复杂,机载测试和评估需求也在大幅增长,尤其是高速大量数据采集需求。随着机载测试平台产生的数据大幅增长,当前传统的航空移动遥测(Aeronautical Mobile Telemetry,AMT)通信设备即将遇到瓶颈期。从长远来看,传统的AMT可能无法跟上现代机载平台日益增长的测试速度和更大操作灵活性的需求。因此,需要突破传统AMT技术来支持机载平台现在以及未来的需求。展开更多
Multi-modal image matching is crucial in aerospace applications because it can fully exploit the complementary and valuable information contained in the amount and diversity of remote sensing images.However,it remains...Multi-modal image matching is crucial in aerospace applications because it can fully exploit the complementary and valuable information contained in the amount and diversity of remote sensing images.However,it remains a challenging task due to significant non-linear radiometric,geometric differences,and noise across different sensors.To improve the performance of heterologous image matching,this paper proposes a normalized self-similarity region descriptor to extract consistent structural information.We first construct the pointwise self-similarity region descriptor based on the Euclidean distance between adjacent image blocks to reflect the structural properties of multi-modal images.Then,a linear normalization approach is used to form Modality Independent Region Descriptor(MIRD),which can effectively distinguish structural features such as points,lines,corners,and flat between multi-modal images.To further improve the matching accuracy,the included angle cosine similarity metric is adopted to exploit the directional vector information of multi-dimensional feature descriptors.The experimental results show that the proposed MIRD has better matching accuracy and robustness for various multi-modal image matching than the state-of-the-art methods.MIRD can effectively extract consistent geometric structure features and suppress the influence of SAR speckle noise using non-local neighboring image blocks operation,effectively applied to various multi-modal image matching.展开更多
文摘1背景随着机载测试平台的能力越来越强,架构越来越复杂,机载测试和评估需求也在大幅增长,尤其是高速大量数据采集需求。随着机载测试平台产生的数据大幅增长,当前传统的航空移动遥测(Aeronautical Mobile Telemetry,AMT)通信设备即将遇到瓶颈期。从长远来看,传统的AMT可能无法跟上现代机载平台日益增长的测试速度和更大操作灵活性的需求。因此,需要突破传统AMT技术来支持机载平台现在以及未来的需求。
基金supported by the National Natural Science Foundation of China,China(No.61801491)。
文摘Multi-modal image matching is crucial in aerospace applications because it can fully exploit the complementary and valuable information contained in the amount and diversity of remote sensing images.However,it remains a challenging task due to significant non-linear radiometric,geometric differences,and noise across different sensors.To improve the performance of heterologous image matching,this paper proposes a normalized self-similarity region descriptor to extract consistent structural information.We first construct the pointwise self-similarity region descriptor based on the Euclidean distance between adjacent image blocks to reflect the structural properties of multi-modal images.Then,a linear normalization approach is used to form Modality Independent Region Descriptor(MIRD),which can effectively distinguish structural features such as points,lines,corners,and flat between multi-modal images.To further improve the matching accuracy,the included angle cosine similarity metric is adopted to exploit the directional vector information of multi-dimensional feature descriptors.The experimental results show that the proposed MIRD has better matching accuracy and robustness for various multi-modal image matching than the state-of-the-art methods.MIRD can effectively extract consistent geometric structure features and suppress the influence of SAR speckle noise using non-local neighboring image blocks operation,effectively applied to various multi-modal image matching.
