Space target recognition based on deep learning

Zeng, Haoyue; Xia, Yong

doi:10.23919/icif.2017.8009786

Cited by 20 publications

(11 citation statements)

References 10 publications

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“…We can see that our GPR model gets similar results as DCNN [33] on BUAA-SID 1.0, not better than ResNet [46] and Dense-Net [47]. As for the BUAA-SID 1.5 dataset, our GPR model achieves better satellite recognition accuracy than DCNN [33], the same 100% performance as ResNet [46] and Dense-Net [47] on 1D the and noise subsets, and slightly worse than ResNet [46] and DenseNet [47] on the lighting subset. This means that deeper networks like ResNet [46] and DenseNet [47] can achieve better recognition results, especially when the classes to be classified enlarge, i.e., the recognition problem becomes harder.…”

Section: Experiments and Analysesmentioning

confidence: 69%

“…Recently, deep learning methods are widely used for general object recognition. Zeng et al [33] introduced such technology for space target recognition. We reproduced the nine-layer deep convolutional neural network in [33] to achieve satellite recognition on our datasets.…”

Section: Experiments and Analysesmentioning

confidence: 99%

“…Zeng et al [33] introduced such technology for space target recognition. We reproduced the nine-layer deep convolutional neural network in [33] to achieve satellite recognition on our datasets. We chosen rotation and crop as the way of data augmentation and also fixed the magnitude of training data at 8-fold.…”

Section: Experiments and Analysesmentioning

confidence: 99%

“…In the past decade, vision-based satellite recognition or identification is attracting more and more interest [12,[17][18][19][30][31][32][33]. Some of these methods focus on extracting good features to improve recognition performance of traditional classifiers like k-nearest-neighbors, support vector machines, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, the other approaches tend to use various machine learning models for a better recognition of the satellites. Zhang and Jiang [19] solved multiview space object recognition by kernel regression, [12] used homeomorphic manifold analysis for satellite recognition, and [33] built a nine-layer deep convolutional neural network DCNN to achieve space target recognition. In particular, [19] and [12] formulated vision-based satellite recognition and pose estimation in one framework.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Vision-Based Satellite Recognition and Pose Estimation Using Gaussian Process Regression

Zhang

Jiang

et al. 2019

International Journal of Aerospace Engineering

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In this paper, we address the problem of vision-based satellite recognition and pose estimation, which is to recognize the satellite from multiviews and estimate the relative poses using imaging sensors. We propose a vision-based method to solve these two problems using Gaussian process regression (GPR). Assuming that the regression function mapping from the image (or feature) of the target satellite to its category or pose follows a Gaussian process (GP) properly parameterized by a mean function and a covariance function, the predictive equations can be easily obtained by a maximum-likelihood approach when training data are given. These explicit formulations can not only offer the category or estimated pose by the mean value of the predicted output but also give its uncertainty by the variance which makes the predicted result convincing and applicable in practice. Besides, we also introduce a manifold constraint to the output of the GPR model to improve its performance for satellite pose estimation. Extensive experiments are performed on two simulated image datasets containing satellite images of 1D and 2D pose variations, as well as different noises and lighting conditions. Experimental results validate the effectiveness and robustness of our approach.

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