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Sugarcane is an important economic crop. After machine harvesting, the impurity rate of sugarcane is an important metric, which affects the sugar output rate. In order to obtain the impurity rate and detect primary impurities, this paper proposes an impurity rate estimation method for post-harvest sugarcane based on rotated bounding box and binocular vision. Firstly, the sugarcane mixture image including sugarcane segments, sugarcane tops, and sugarcane leaves was captured by a binocular camera. Secondly, we replaced the Darknet53 backbone with the EfficientNetV2 network in the improved YOLOv5-obb algorithm to obtain the rotated bounding boxes for the sugarcane mixture. Next, based on binocular vision, the dimensions of sugarcane segments are calculated. And the sugarcane segments are fitted as cylinders, enabling the calculation of their volume and mass. Finally, the impurity rate of post-harvest sugarcane is calculated based on the mass of sugarcane segments and the total mass of the mixture. Experimental results show that rotated bounding boxes can fit the shape of each target accurately, with a mean average precision (mAP) of 97.6%. The model also effectively detects occluded and overlapping targets, while reducing parameters by 36% compared to YOLOv5-obb. The average detection time per image is 0.017 s, and the average time for impurity rate estimation per image is 0.19 s. For 830 test images, the average mass error of sugarcane segments is 10.31%, the total mass error is 0.23%, and the total impurity rate error is 4.58%.

Accurate surface deformation (SD) predictions are critical for early warning and timely remediation of infrastructure damage. However, the current SD prediction models do not integrate the multiscale features of InSAR SD and environmental factors (EFs), which make their prediction results inaccurate. To address these limitations, we proposed a bidirectional gated recurrent unit (BiGRU) multioutput SD prediction network (GLER-BiGRUnet), which mainly included global–local feature extraction (GLFE), multifactor cross-attention residual (MCAR), and local residual module embedded in self-attention mechanism (RCSA) modules. Specifically, dense and one-dimensional convolutional layers were concatenated in the GLFE module to extract global–local SD features. The long time-series dependence between EFs and SD was learned in the MCAR module using the multihead cross-attention mechanism to obtain the corresponding attention weight feature matrix. The residual connection and self-attention mechanisms were used in the RCSA module to merge the multiscale features and enhance the model fitting ability. We chose four typical regions in the permafrost area of Qinghai–Tibet Railway as the scene for the experiment. The spatial distribution and local profile exhibited relatively small discrepancies between the prediction results of the GLER-BiGRUnet model and the InSAR SD. Meanwhile, the average root-mean-square error of the GLER-BiGRUnet model in the four typical regions was 0.19 mm, and the proposed model had the best evaluation index compared with other SD prediction models. Additionally, the prediction trend of SD of the proposed GLER-BiGRUnet model was consistent with the original InSAR SD, and the prediction results were more stable than those of the other prediction models. The SD prediction model proposed in this article contributes to early warning of SD.

Landslide susceptibility mapping (LSM) is of great significance in geohazard early warning and prevention. The existing LSM methods mostly used traditional static landslide conditioning factors (LCFs), which only considered the spatial features of single-pixel neighborhoods and could not extract the time-series dynamic features of developing landslides, resulting in low accuracy and insufficient reliability of LSM. To solve this problem, this study proposes to introduce time-series rainfall factors based on the traditional static factors and construct an integrated time-series dynamic neural network (TSDNN) model for LSM. A convolutional neural network (CNN) adding time-distributed convolution and a bidirectional long and short-term memory neural network is utilized to extract time-series rainfall features, and a multiscale convolutional neural network (MSCNN) is utilized to extract static features of the static LCFs. In this study, multicollinearity analysis and geodetector are utilized to analyze the LCFs. Multiple evaluation metrics are utilized to analyze the proposed model performance. The results indicate that the overall accuracy has improved by introducing time-series rainfall factors, and the susceptibility area of actual predicted is more refined. The study indicates that significant advantages of the proposed TSDNN model are over models [CNN, MSCNN, random forest (RF)] when processing combined static and rainfall data. This is notably evident that the accuracy is enhanced by 12.9%, 10.7%, and 11.4% compared to CNN, MSCNN, and RF models in the receiver operating characteristic curve analysis, respectively. Moreover, two typical areas containing three recent landslide events validate the reliability of the proposed TSDNN model. The proposed network model framework for LSM considering time-series rainfall factors can provide new ideas and key technical support for landslide disaster prevention.

Surface deformation of alpine peatland in China has an important effect on runoff and is of great significance for wetland ecosystem protection. However, spatio-temporal characteristics of alpine peatland surface deformation in China lack systematic studies, and the driving mechanism is not yet clear. In this study, we selected the alpine peatland of Maduo County in China as the research object, surface deformation of peatland based on the small baseline subset radar interferometry technique was obtained, we analyzed spatio-temporal deformation characteristics and patterns of peatland, explored the driving mechanism of the peatland surface deformation with single-factor and multifactor combinations of Geo-detector, respectively. The results showed that the overall subsidence rates of peatlands in Maduo County, China slowed down year by year from 2018 to 2020, but there was seasonal freezing and thawing, subsidence rates of peatlands at high elevation and high slopes were stable, peatlands at low elevation and low slope were vulnerable to disturbance, subsidence rates are largest. Maliecuo, Bailongqu, and Gaerlawangzang regions were serious subsidence, the maximum subsidence rate was 159 mm/year. Meteorological factors and geological conditions were the main reasons for the surface deformation of alpine peatland in Maduo County, China. This study provides a theoretical basis for the conservation and restoration of peatland ecosystems in the alpine regions of China.

The ancient Suoertou landslide seriously threatens the surrounding population's lives and property. Monitoring and predicting this landslide is crucial to ensure the affected areas’ safety. The previous research on the landslide's displacement characteristics and mechanisms has lacked detailed analyses. In addition, its future development trends must be understood. Therefore, we conducted a detailed analysis of the ancient Suoertou landslide's displacement characteristics and mechanisms using small baseline subset interferometric synthetic aperture radar monitoring results from 2018 to 2023. Furthermore, by applying a gated recurrent unit prediction mode that incorporates the refined displacement characteristics and mechanisms, we forecasted the landslide's displacement trends. The results show that this landslide is currently undergoing overall slow displacement with violent fluctuations in localized areas. Tectonic movement, precipitation, human activities, river erosion, and other factors interact, forming a vicious development displacement mechanism. According to our prediction, the displacement of this landslide will be in a trend of fluctuating increase from June 2023 to June 2024. In particular, the local area will undergo abnormal displacement acceleration. The results of the current research provide a scientific basis upon which to monitor landslides, promote their management, and reduce the risk of losses due to landslide disasters.

Accurate and efficient landslide identification is an important basis for landslide disaster prevention and control. Due to the diversity of landslide features, vegetation occlusion, and the complexity of the surrounding surface environment in remote sensing images, deep learning models (such as U-Net) for landslide detection based only on optical remote sensing images will lead to false and missed detection. The detection accuracy is not high, and it is difficult to satisfy the demand. Synthetic aperture radar (SAR) has penetrability, and SAR images are highly sensitive to changes in surface morphology and structure. In this study, a multi-input channel U-Net landslide detection method fusing SAR, optical, and topographic multisource remote sensing data is proposed. First, a multi-input channel U-Net model fusing SAR multisource remote sensing data is constructed, then an attention mechanism is introduced into the multi-input channel U-Net to adjust the spatial weights of the feature maps of the multisource data to emphasize the landslide-related features, and finally, the proposed model is applied to the experimental scene for validation. The experimental results demonstrate that the proposed model combined with SAR multisource remote sensing data improves the perception ability of landslide features, focuses on learning landslide-related features, improves the accuracy of landslide detection, and reduces the rate of false detections and missed detections. Compared with the traditional U-Net landslide detection method based on SAR multisource remote sensing data and the traditional U-Net method that disregards SAR multisource remote sensing data, the proposed method has the best quantitative evaluation indicators. Among them, the proposed method obtained the highest F1 value (66.18%), indicating that fused SAR remote sensing data can provide rich and complementary landslide feature information, simultaneously setting up a multichannel U-Net model to input multisource remote sensing data can effectively process landslide feature information. The proposed method can provide theoretical and technical support for landslide disaster prevention and control.

This paper presents a control algorithm for a micro-electro-mechanical system (MEMS) fast steering mirror (FSM), whose performance is poor in open loop control mode. Based on the characterization and parameter identification of MEMS FSM, an improved double step algorithm is proposed to shorten the settling time and improve its quasi-static working bandwidth. A characterization system is set up, in which an optical sensor is utilized to monitor the tilt angle of FSM. The performance of the FSM in the time and frequency domain are both acquired, while the critical parameters of the FSM model are identified. Finally, the closed-loop experiments for the FSM are done to verify the effectiveness of the control algorithm. The experimental results demonstrate that the settling time (98%) of the FSM is effectively reduced from 398 ms to 0.4 ms under the improved double step control mode, which has a better tracking performance and tiny overshoot, compared with the open loop control mode.

In this paper, a MMP algorithm is proposed. Compared with the classical algorithm, the proposed algorithm reduces the noise threshold of stably extractable parameters by 15 dB and reduces the computing time by ten or more. As an efficient way to interpret the measurements of high-frequency Inverse Synthetic Aperture Radar (ISAR), the Geometric Theory of Diffraction (GTD) model provides concise features of complex targets. However, the existing parameter extraction algorithms suffer from high computational complexity and poor ability against noise. To solve these challenges, a Maximum Matching Pursuit (MMP) algorithm is proposed in this paper. The proposed algorithm achieves parameter estimation by searching the maximum value of the dictionary matrix and observation signal matrix product. Compared to the classical OMP algorithm, the proposed algorithm significantly reduces the computational complexity by estimating params without cycles. To demonstrate the reconstruction efficiency of the improved algorithm, the Root-Mean-square-error (RMSE), and the computer time of the proposed algorithms are compared with the original algorithms, such as the OMP and the Estimating Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm, under different Signal-to-Noise-Ratio (SNR). The simulation results show that the proposed algorithm reduces the noise threshold of stably extractable parameters by 15 dB, reducing the computing time by ten or more.

Surface deformation poses a great threat to the safety of Jinchuan mining area production activities. At present, the spatio-temporal evolution law and mechanism of surface deformation in the Jinchuan mining area are unclear, and it is difficult to obtain reliable prediction results using the existing spatio-temporal prediction methods due to the lack of model parameters or relevant data. To solve these problems, this study proposes a new unified convolutional neural network with peephole long short-term memory (CNN-PhLSTM). Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology was used to obtain the spatio-temporal evolution characteristics of surface deformation in the period of 2014–2021. Time series InSAR deformation data are merged into a unified network model in series with a time-distributed CNN segmentation and stacked PhLSTM. The InSAR measurement results are shown to be reliable by comparison and verification with the benchmark and InSAR results of different orbits. The proposed CNN-PhLSTM model was evaluated by mean absolute error and structural similarity (SSIM) evaluation indexes, and was compared with support vector regression (SVR), multilayer perceptron (MLP) and CNN-LSTM models. The results show three continuous subsidence areas, namely the Longshou, second western and third eastern mining areas. The cumulative surface deformation continued to increase from 2014 to 2021. Faults and lithology control the spatial distribution of surface deformation in the Jinchuan mining area. The prediction results demonstrate that the surface deformation range will continue to expand and that time-series surface deformation will show a slow deceleration trend in the next two years.

Interferometric phase filtering is a crucial step in the interferometric synthetic aperture radar (InSAR) data processing, which is also important for improving the accuracy of topography mapping and deformation monitoring. Most of the commonly used phase filtering methods perform windowing computations based on the statistical characteristics of a single interferogram in the spatial or frequency domain. However, the difficulty in taking into account the diversity and complexity of the phase image results in filtering methods with weak denoising, limited detail preservation, and poor generalization ability. At the same time, regardless of the spatial or frequency domain, improved phase filtering performance inevitably leads to the problem of declining effectiveness. This article proposes a phase filtering method based on the deep convolution neural network with multiscale feature dynamic fusion (MSFF). Unlike the traditional feedforward neural networks, the proposed method adopts a strategy of multiscale feature dynamic fusion that accounts for the deep and shallow features of the interferometric phase while also taking into account image detail preservation and noise suppression during phase filtering. Based on both subjective and objective evaluations, the experimental results using the simulated data prove that the proposed method has better noise suppression and detail preservation than the commonly used methods and that the filtering performance is less dependent on noise level. Experiments using the real data confirm that the proposed method has better generalization ability and can meet the precision requirements of practical applications. The method presented in this article can provide a new approach for research in high-precision InSAR data processing technology while also offering technical support for practical InSAR applications.