Identifying and segmenting spacecraft components is vital in many on-orbit space missions,such as on-orbit maintenance and component recovery.Integrating depth maps with visual images has been proven effective in impr...Identifying and segmenting spacecraft components is vital in many on-orbit space missions,such as on-orbit maintenance and component recovery.Integrating depth maps with visual images has been proven effective in improving segmentation accuracy.However,existing methods ignore the noise and fallacy in collected depth maps,which interfere with the network to extract representative features,decreasing the final segmentation accuracy.To this end,this paper proposes a Filtering and Regret Network(FRNet)for spacecraft component segmentation.The FRNet incorporates filtering and regret mechanisms to suppress the abnormal depth response in shallow layers and selectively reuses the filtered cues in deep layers,avoiding the detrimental effects of low-quality depth information while preserving the semantic context inherent in depth maps.Furthermore,a two-stage feature fusion module is proposed,which involves information interaction and aggregation.This module effectively explores the feature correlation and unifies the multimodal features into a comprehensive representation.Finally,a large-scale spacecraft component recognition dataset is constructed for training and evaluating spacecraft component segmentation algorithms.Experimental results demonstrate that the FRNet achieves a state-of-the-art performance with a mean Intersection Over Union(mIOU)of 84.13%and an average inference time of 133.2 ms when tested on an NVIDIA RTX 2080 SUPER GPU.展开更多
This paper proposes a PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm. This algorithm combines the linear feature extracted from principal component analysis （PCA） and nonlinear featu...This paper proposes a PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm. This algorithm combines the linear feature extracted from principal component analysis （PCA） and nonlinear feature extracted from kernel principal component analysis （KPCA） respectively, and then utilizes the adaptive feature fusion algorithm which is based on the weighted maximum margin criterion （WMMC） to fuse the features in order to achieve better performance. The linear regression classifier is used in the experiments. The experimental results indicate that the proposed self-fusion algorithm achieves higher recognition rate compared with the traditional PCA and KPCA feature fusion algorithms.展开更多
文摘Identifying and segmenting spacecraft components is vital in many on-orbit space missions,such as on-orbit maintenance and component recovery.Integrating depth maps with visual images has been proven effective in improving segmentation accuracy.However,existing methods ignore the noise and fallacy in collected depth maps,which interfere with the network to extract representative features,decreasing the final segmentation accuracy.To this end,this paper proposes a Filtering and Regret Network(FRNet)for spacecraft component segmentation.The FRNet incorporates filtering and regret mechanisms to suppress the abnormal depth response in shallow layers and selectively reuses the filtered cues in deep layers,avoiding the detrimental effects of low-quality depth information while preserving the semantic context inherent in depth maps.Furthermore,a two-stage feature fusion module is proposed,which involves information interaction and aggregation.This module effectively explores the feature correlation and unifies the multimodal features into a comprehensive representation.Finally,a large-scale spacecraft component recognition dataset is constructed for training and evaluating spacecraft component segmentation algorithms.Experimental results demonstrate that the FRNet achieves a state-of-the-art performance with a mean Intersection Over Union(mIOU)of 84.13%and an average inference time of 133.2 ms when tested on an NVIDIA RTX 2080 SUPER GPU.
基金supported in part by the National Natural Science Foundation of China under Grant No. 61033012, No. 611003177, and No. 61070181Fundamental Research Funds for the Central Universities under Grant No.1600-852016 and No. DUT12JR07
文摘This paper proposes a PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm. This algorithm combines the linear feature extracted from principal component analysis （PCA） and nonlinear feature extracted from kernel principal component analysis （KPCA） respectively, and then utilizes the adaptive feature fusion algorithm which is based on the weighted maximum margin criterion （WMMC） to fuse the features in order to achieve better performance. The linear regression classifier is used in the experiments. The experimental results indicate that the proposed self-fusion algorithm achieves higher recognition rate compared with the traditional PCA and KPCA feature fusion algorithms.
