The recognition algorithm of the lightning whistler wave, based on intelligent speech, is the key technology to break the bottleneck of massive data and study the temporal and spatial variation rules of the lightning whistler wave. However, its recognition effect depends on the hyperparameters determined by manual experiments repeatedly, which takes a great deal of time and cannot guarantee the best recognition effect of the model. Therefore, we proposed the lightning whistler wave recognition algorithm based on grey wolf optimization (GWO). In this paper, the GWO algorithm is used to automatically find the best value of hyperparameters of Long Short-Term Memory (LSTM) in their limited searching space. Here we consider the number of hidden units (hu) and learning rate (lr) as the hyperparameters to be optimized, and the spatial coordinate (hu, lr) as the grey wolf position. By the end of the GWO process, we obtain the position of the wolf king α with the optimal hu and lr searched by the GWO algorithm. Then we use the optimal hu and lr to configure LSTM and perform supervised learning on the train set to obtain the final lightning whistler wave speech recognition model. Through experimental verification, the recognition model based on the GWO not only overcomes the uncertainty of the traditional model relying on manual finetuning of parameters and realizes the mechanism of automatic search and acquisition of hyperparameters, but also its recognition effect improves by about 2% in accuracy, F1score, and other metrics compared with the model trained by manually setting hyperparameters.

As important disaster-bearing bodies, buildings are the focus of attention in seismic disaster risk assessment and emergency rescue. It is of great practical significance to extract buildings quickly and accurately with complex textures and variable scales and shapes from high-resolution remote sensing images. We proposed an improved TransUnet model based on multiscale grouped convolution and attention named MATUnet to retain more local detail features and enhance the representation ability of global features, while reducing the network parameters. We designed the multiscale grouped convolutional feature extraction module with attention (GAM) to enhance the representation of detailed features. The convolutional positional encoding module (PEG) was added to redetermine the number of transformer, it solved the problem of local feature information loss and the difficulty of convergence of the network. The channel attention module (CAM) of the decoder enhanced the salient information of the features and solved the problem of information redundancy after feature fusion. We experimented through MATUnet on the WHU building dataset and Massachusetts dataset. MATUnet achieved the best IOU results of 92.14% and 83.22%, respectively, and achieved better than the other generalized and state-of-the-art networks under the same conditions. We also have achieved good segmentation results on the GF2 Xichang building dataset.

Lightning whistler waves, as an important tool for geospace exploration, can be found from the vast amount of electromagnetic satellite data. In recent years, with the development of computer vision and deep learning technologies, some advanced  algorithms have been developed to automatically identify lightning whistler waves from the massive archived data of electromagnetic satellites. However, these algorithms fail to automatically extract the dispersion coefficients of lightning whistlers(DCW). Since the DCW are depended on the propagation path of lightning and geospace environments, it is extremely important for further geospace exploration.We proposed an algorithm that can automatically extract the dispersion coefficients of lightning whistler: (1) using two seconds time window on the SCM VLF data from the ZH-1 satellite to obtain segmented data; (2) generating time-frequency profile (TFP) of the segmented waveform by performing a band-pass filter and the short-time Fourier transform with a 94% overlap; (3) annotating the ground truth of the whistler with the rectangular boxes on the each time-frequency image to construct the training dataset; (4) building the YOLOV3 deep neural network and setting the training parameters; (5) inputting the training dataset to the YOLOV3 to train the whistler recognition model; (6) detecting the whistler from the unknown time-frequency image to extract the whistler area with the rectangle box as a sub-image; (7) conducting the BM3D algorithm to denoise the sub-image; (8) employing an adaptive threshold segmentation algorithm on the denoised sub-image to obtain the binary image which represents the whistler trace with the black pixel and other area with white pixel. (9) removing isolated points in the binary image with the open operation in morphology; (10) extracting lightning whistler trajectory region using connected domain analysis; (11) converting the trajectory coordinates from (t-f) to (f-0.5-t); (12) taking into account the Eckersley formula, which depicts the relationship between the scattering coefficient and the time frequency, we use the least-squares method on the converted trajectory coordinates to fit a straight line and obtain the slope of the line as the dispersion coefficient.In order to evaluate the effectiveness of the proposed algorithm, we construct two dataset: a simulation set and an observational dataset. The simulation set is composed of 1000 lightning whistler trajectories, which are generated according to the Eckersley formula. The observational dataset containing 1000 actual single-trace lightning whistler, are generated by collecting the data from the SCM VLF from the ZH-1 satellite. The experiment results show that: the mean-square error on the simulation set is below 2.8x 10-4; The mean-square error on the observational dataset is below 2.1054x10-3.Keywords: ZH-1 Satellite, SCM, Lightning Whistler, YOLOV3, Dispersion Coefficients

Lightning whistlers, found frequently in electromagnetic satellite observation, are the important tool to study electromagnetic environment of the earth space. With the increasing data from electromagnetic satellites, a considerable amount of time and human efforts are needed to detect lightning whistlers from these tremendous data. In recent years, algorithms for lightning whistlers automatic detection have been conducted. However, these methods can only work in the time-frequency profile (image) of the electromagnetic satellites data with two major limitations: vast storage memory for the time-frequency profile (image) and expensive computation for employing the methods to detect automatically the whistler from the time-frequency profile. These limitations hinder the methods work efficiently on ZH-1 satellite. To overcome the limitations and realize the real-time whistler detection automatically on board satellite, we propose a novel algorithm for detecting lightning whistler from the original observed data without transforming it to the time-frequency profile (image).The motivation is that the frequency of lightning whistler is in the audio frequency range. It encourages us to utilize the speech recognition techniques to recognize the whistler in the original data \of SCM VLF Boarded on ZH-1. Firstly, we averagely move a 0.16 seconds window on the original data to obtain the patch data as the audio clip. Secondly, we extract the Mel-frequency cepstral coefficients (MFCCs) of the patch data as a type of cepstral representation of the audio clip. Thirdly, the MFCCs are input to the Long Short-Term Memory (LSTM) recurrent neutral networks to classification. To evaluate the proposed method, we construct the dataset composed of 10000 segments of SCM wave data observed from ZH-1 satellite(5000 segments which involving whistler and 5000 segments without any whistler). The proposed method can achieve 84% accuracy, 87% in recall, 85.6% in F1score.Furthermore, it can save more than 126.7MB and 0.82 seconds compared to the method employing the YOLOv3 neutral network for detecting whistler on each time-frequency profile. Key words: ZH-1 satellite, SCM,lightning whistler, MFCC, LSTM

Sparse unmixing of hyperspectral images aims to separate the endmembers and estimate the abundances of mixed pixels. This approach is the essential step for many applications involving hyperspectral images. The multi scale spatial sparse hyperspectral unmixing algorithm (MUA) could achieve higher accuracy than many state-of-the-art algorithms. The regularization parameters, whose combinations markedly influence the unmixing accuracy, are determined by manually searching in the broad parameter space, leading to time consuming. To settle this issue, the adaptive multi-scale spatial sparse hyperspectral unmixing algorithm (AMUA) is proposed. Firstly, the MUA model is converted into a new version by using of a maximum a posteriori (MAP) system. Secondly, the theories indicating that andnorms are equivalent to Laplacian and multivariate Gaussian functions, respectively, are applied to explore the strong connections among the regularization parameters, estimated abundances and estimated noise variances. Finally, the connections are applied to update the regularization parameters adaptively in the optimization process of unmixing. Experimental results on both simulated data and real hyperspectral images show that the AMUA can substantially improve the unmixing efficiency at the cost of negligible accuracy. And a series of sensitive experiments were undertook to verify the robustness of the AMUA algorithm.

张衡卫星日产数据高达20 GByte, 人工方法已经无法满足海量数据提取的需求. 提出了一种快速稳健的闪电哨声波散射系数自动提取方法. 将原始数据转换为时频图和音频文件; 针对时频图, 创建YOLOV5神经网络自动定位闪电哨声波, 输出其时频位置信息; 根据其时频位置信息截取对应音频文件得到含有闪电哨声波的音频数据, 并用零将其填充为0.8 s的音频片段; 提取音频片段的梅尔频率倒谱系数, 将其输入多头注意力机制改进的门控循环网络, 自动提取闪电哨声波散射系数. 将该方法应用于2020年2月的数据集, 得到如下结果: 其平均绝对误差和平均绝对百分比误差为0.453和0.176, 相较于文献[1]方法降低了70%和 46%. 每段数据平均处理时间为0.074 s, 相较于文献[1]方法降低了92%. 本文提出的自动音频分析方法, 可较为准确和快速地提取闪电哨声波的散射系数.