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The current satellite management system mainly relies on manual work. If small faults cannot be found in time, it may cause systematic fault problems and then affect the accuracy of satellite data and the service quality of meteorological satellite. If the operation trend of satellite will be predicted, the fault can be avoided. However, the satellite system is complex, and the telemetry signal is unstable, nonlinear, and time-related. It is difficult to predict through a certain model. Based on these, this paper proposes a bidirectional long short-term memory (BiLSTM) deep leaning model to predict the operation trend of meteorological satellite. In the method, the layer number of the model is designed to be two, and the prediction results, which are forecasted by LSTM network as the future trend data and historical data, are both taken as the input of BiLSTM model. The dataset for the research is generated and transmitted from Advanced Geostationary Radiation Imager (AGRI), which is the load of FY4A meteorological satellite. In order to demonstrate the superiority of the BiLSTM prediction model, it is compared with LSTM based on the same dataset in the experiment. The result shows that the BiLSTM method reports a state-of-the-art performance on satellite telemetry data.

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Robust semantic segmentation of VHR remote sensing images from UAV sensors is critical for earth observation, land use, land cover or mapping applications. Several factors such as shadows, weather disruption and camera shakes making this problem highly challenging, especially only using RGB images. In this paper, we propose the use of multi-modality data including NIR, RGB and DSM to increase robustness of segmentation in blurred or partially damaged VHR remote sensing images. By proposing a cascaded dense encoderdecoder network and the SELayer based fusion and assembling techniques, the proposed RobustDenseNet achieves steady performance when the image quality is decreasing, compared with the state-of-the-art semantic segmentation model.

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At present, satellite anomaly is mostly solved after the event, and rarely predicted in advance in satellite health management. Thus, satellite trend prediction is quietly important for avoiding the fault which perhaps affects data accuracy and service quality of satellite, and even impacts greatly on satellite safety. However, it is difficult to predict satellite operation through a simple model because satellite system is complex, and telemetry data is numerous, coupled and spatiotemporal. Therefore, this paper proposes a model combing attention mechanism and Bidirectional Long Short-term Memory (Attention-BiLSTM) with correlation telemetry to predict the situation of satellite operation. Firstly, high-dimensional K-NearestNeighbor Mutual Information (HKNN-MI) method is performed to select the related telemetry variables from multiple variables of satellite telemetry data. Secondly, we put forward to a new BiLSTM model with attention mechanism for telemetry prediction. The dataset for the research is generated and transmitted from the power system of FY3E meteorological satellite. In order to verify the superiority of the proposed model, it is compared with other method based on the same dataset in the experiment. The result shows that the method outperforms other methods due to its better accuracy and prediction precision.

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