space situational awareness (SSA) system requires recognition of spaceobjects that are varied in sizes, shapes, and types. The space images are challenging because of several factors such as illumination and noise an...
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space situational awareness (SSA) system requires recognition of spaceobjects that are varied in sizes, shapes, and types. The space images are challenging because of several factors such as illumination and noise and thus make the recognition task complex. Image fusion is an important area in image processing for various applications including RGB-D sensor fusion, remote sensing, medical diagnostics, and infrared and visible image fusion. Recently, various image fusion algorithms have been developed and they showed a superior performance to explore more information that are not available in single images. In this paper, we compared various methods of RGB and Depth image fusion for space object classification task. The experiments were carried out, and the performance was evaluated using 13 fusion performance metrics. It was found that the guided filter context enhancement (GFCE) outperformed other image fusion methods in terms of average gradient (8.2593), spatial frequency (28.4114), and entropy (6.9486). additionally, due to its ability to balance between good performance and inference speed (11.41 second), GFCE was selected for RGB and Depth image fusion stage before feature extraction and classification stage. The outcome of fusion method is fused images that were used to train a deep ensemble of CoAtNets to classify spaceobjects into ten categories. The deep ensemble learning methods including bagging, boosting, and stacking were trained and evaluated for classification purposes. It was found that combination of fusion and stacking was able to improve classification accuracy largely compared to the baseline methods by producing an average accuracy of 89 % and average F1 score of 89 %.
The purpose of this study is to present agile, intelligent, and efficient computer vision architectures, operating on quantum neuromorphic computing, as part of a space Situational Awareness (SSA) network. Quantum neu...
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ISBN:
(纸本)9781665481021
The purpose of this study is to present agile, intelligent, and efficient computer vision architectures, operating on quantum neuromorphic computing, as part of a space Situational Awareness (SSA) network. Quantum neuromorphic vision paired with polarimetric Dynamic Vision Sensors p(DVS) principles, would give rise to the next generation of highly efficient neuromorphic engineering vision systems for SSA, at fast speeds, while operating at reduced bandwidth, low-power, and low-memory. A deep-learning network has been designed with high accuracy to classify different target speeds and shapes, by means of a p(DVS) neuromorphic sensor. The neural network relies on a limited number of events, within a fixed time window, instead of full frame images. In addition, it makes use of two classifiers, which practically take a single input and independently classify both its speed and shape. The outcome of this study indicates that both high computational efficiency and target classification accuracy results.
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