To effectively detect and identify the anomaly data in massive satellite telemetry data sets, the novel detection and identification method based on the pseudo-period was proposed in this paper. First, the raw data were compressed by extracting the shape salient points. Second, the compressed data were symbolized by the tilt angle of the adjacent data points. Based on this symbolization, the pseudo-period of the data was extracted. Third, the phase-plane trajectories corresponding to the pseudo-period data were obtained by using the pseudo-period as the basic analytical unit, and then, the phase-plane was divided into statistical regions. Finally, anomaly detection and identification of the raw data were achieved by analyzing the statistical values of the phase-plane trajectory points in each partition region. This method was verified by a simulation test that used the measured data of the satellite momentum wheel rotation. The simulation results showed that the proposed method could achieve the pseudo-period extraction of the measured data and the detection and identification of the anomalous telemetry data.

In this letter, a novel morphological band selection method is proposed to obtain the most representative bands from hyperspectral image (HSI) in an unsupervised manner. In order to sufficiently process the HSI, we propose to use only a small set of data instead of using the original full data. For the obtained clusters, the differences of spectral response curves are applied to measure the local discrimination capability of bands, of which the local maximum value point is yielded based on the morphological processing. To verify the performance of the proposed method, the robustness of the parameters has been evaluated, while the effectiveness and superiority have been tested on three popular hyperspectral data sets. The experiment results have shown that the proposed method outperforms other methods.

In the field of remote sensing, 3D point clouds provide precise environmental perception information. However, their non-uniformity and irregularity present significant processing challenges. Most current point cloud processing algorithms use the K-Nearest Neighbor (KNN) method to select neighboring points before feature extraction. However, the neighboring points selected by KNN are fixed, meaning the receptive field is a regular spherical shape. This rigidity prevents flexible adaptation to variations in object shapes and can lead to information confusion during subsequent feature propagation. To address this issue, we propose a novel Direction-Aware KNN (DAKNN) algorithm, which manipulates the shape of the receptive field during the neighbor selection process to adaptively focus on the directions where object features are most prominent. Based on the DAKNN algorithm, we designed a feature extraction framework that uses a unified scaling factor to adjust the shape of the receptive field. This approach introduces more diverse directional focus features with fewer points, enhancing the discriminability of features for different objects. It is worth noting that the proposed direction-aware feature extraction framework is general and can be integrated into most existing encoder-decoder network structures. Experimental results on the STPLS3D and S3DIS dataset demonstrate that adopting the proposed framework significantly improves the performance of existing methods.