Journals >Spacecraft Recovery & Remote Sensing
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2024
Volume: 45 Issue 6
13 Article(s)
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[in Chinese]
A Speed Anti-disturbance Control Algorithm for Permanent Magnet AC Servo Systems
Huan LIU, Zhongyang HAO, Xiaomin ZHANG, and Xinhao WANG
The speed controller is an extremely important segment in servo control systems, and its performance is an important factor in measuring the control effect of the entire control system. In practical engineering, there is a large amount of interference in permanent magnet AC servo system, and traditional PID control algThe speed controller is an extremely important segment in servo control systems, and its performance is an important factor in measuring the control effect of the entire control system. In practical engineering, there is a large amount of interference in permanent magnet AC servo system, and traditional PID control algorithms are difficult to achieve satisfactory control results. To improve the anti-interference control performance of permanent magnet AC servo systems, this paper designs a speed anti-interference control algorithm based on an extended state observer. Firstly, the extended state observer is designed to accurately estimate interference in real time while realizing demodulation, and feedforward compensation is performed. Then, a non-singular terminal sliding mode controller is designed to achieve high-precision anti-interference speed control. Simulation and experimental results show that the speed control error of this control algorithm is reduced by more than 64% compared to traditional PID controllers. Therefore, the proposed method has better control performance than the traditional PID control methods, and can improve the anti-interference performance and control accuracy of permanent magnet AC servo systems in practical engineering..
Spacecraft Recovery & Remote Sensing
- Publication Date: Dec. 30, 2024
- Vol. 45, Issue 6, 47 (2024)
The Research of On-orbit MTF Automatic Detection of Satellite Remote Sensing Image Based on NSST
Shuqi YANG, Wenwen QI, Shixiang CAO, and Hongyan HE
When detecting MTF from satellite remote sensing image, it is time-consuming to select e edge of natural objects manually, and easy to be disturbed by human factors, so the test results are unstable. Aimed at eliminating above problems, an automatic MTF detection method for satellite remote sensing images based on Non-When detecting MTF from satellite remote sensing image, it is time-consuming to select e edge of natural objects manually, and easy to be disturbed by human factors, so the test results are unstable. Aimed at eliminating above problems, an automatic MTF detection method for satellite remote sensing images based on Non-Subsampled Shearlet Transform (NSST) is proposed in this paper. Firstly, the image is decomposed into multi-scale and multi-directional components, and multi-scale product filtering and composite morphological operators are used to process the high and low frequency component images respectively. Then conduct research on the entire process from edge image extraction to MTF calculation, gradually screening the edges. Finally, the NSST edge extraction method isused to compare the MTF detection results of medium and high-resolution images with various typical edge operators. The method is applied to target, medium, and high-resolution images for MTF detection. The experimental results show that the NSST edge extraction method removed invalid information such as gradient areas and noise, and the obtain edge lines meet the detection requirements of clear edge lines, uniform edges, and a certain range of inclination angles in the edge method. The MTF automatic detection results applied to target images are consistent with the published data. The standard deviation of MTF automatic detection results for the same terrain edge area is 0.0043 multiple times, and the standard deviation is manually calculated using general software to be 0.0101 . The satellite remote sensing image MTF automatic detection method based on NSST has stronger error stability and is suitable for target images. Through multi scenario calculation and statistical analysis, the MTF detection efficiency has been enhanced more than double..
Spacecraft Recovery & Remote Sensing
- Publication Date: Dec. 30, 2024
- Vol. 45, Issue 6, 56 (2024)
Airport Aircraft Target Tracking Method Based on Joint Surveillance of Low Earth Orbit Satellites
Jiajian CHEN, Yong LIU, Pengyu GUO, Lu CAO... and Ling MENG|Show fewer author(s)
With the development of large-scale commercial remote sensing Low Earth Orbit Satellite Constellations, satellites are capable to work in relay observation mode over the same ground area within a shorter period, which enables continuous monitoring of aircraft targets at airports. This paper proposes a two-stage aircrafWith the development of large-scale commercial remote sensing Low Earth Orbit Satellite Constellations, satellites are capable to work in relay observation mode over the same ground area within a shorter period, which enables continuous monitoring of aircraft targets at airports. This paper proposes a two-stage aircraft target tracking method based on YOLOv7 and DeepSORT for airport aircraft target tracking under joint monitoring of low orbit satellites. By introducing an attention mechanism to improve the YOLOv7 network, accurate detection of small aircraft targets in satellite remote sensing images is achieved; By optimizing the matching mechanism and improving the DeepSORT algorithm, continuous tracking of moving aircraft targets in image sequences with long time differences can be achieved. This article uses satellite images sequences captured by multiple Jilin-1 satellites for algorithm validation. The experimental results show that the method proposed in this paper can integrate imaging information from multiple satellites at different times, improve the continuous perception ability of airport operation status, and achieve a multi-target tracking accuracy of 79.15%, which is more than 3.2% higher than other commonly used tracking algorithms. At the same time, it shows the significant application potential of large-scale low-Earth orbit commercial remote sensing satellite constellations in the field of objects tracking..
Spacecraft Recovery & Remote Sensing
- Publication Date: Dec. 30, 2024
- Vol. 45, Issue 6, 70 (2024)
Image Enhancement-Based Method for Detecting Spatial Targets in Ground-Based Observations
Zhilong YAN, Shixiang CAO, Chunxiao ZHANG, Dou SUN, and Hong LIU
In the task of detecting spatial targets from ground-based observation images, the degradation or blurring of the star image will lead to unsatisfactory detection results, and the image enhancement methods based on filtering and denoising are difficult to apply to the enhancement of the star image that is degraded or bIn the task of detecting spatial targets from ground-based observation images, the degradation or blurring of the star image will lead to unsatisfactory detection results, and the image enhancement methods based on filtering and denoising are difficult to apply to the enhancement of the star image that is degraded or blurred due to various factors, which will affect the subsequent target detection tasks. In order to solve the above problems, this paper proposes a spatial objects detection method for ground-based observation based on image enhancement, which can be divided into two parts: 1) In the image enhancement part, inspired by the image dehazing deep learning model, an image enhancement model is designed to estimate the degradation parameters, and the model uses U-Net as the basic structure for multi-scale feature extraction and fusion, and embeds the pyramid pooling module and the structure multi-scale residual module in the network structure to improve the quality of image enhanced and restored. Finally, the estimated parameters are used to restore the enhanced image from the degraded image. 2) In the target detection part, the FLD (Fast Line segment Detector) algorithm is used to detect the straight line trajectory left by the relative motion of the target and the detector in the star image. The experimental dataset verifies that the average PSNR (Peak Signal-to-Noise Ratio) and SSIM(Structural SIMilarity) of the images enhanced by our method are increased by 124.51% and 64.28%, respectively. Before and after image enhancement and restoration, the average accuracy of object detection is increased by 22.15%, and the average missed detection rateis decreased by 20.6%. Compared with other image enhancement methods based on filtering and denoising, this method can adapt to a variety of star image degradation or star image blurring situations, has better image enhancement effect, and also achieves higher accuracy in target detection tasks..
Spacecraft Recovery & Remote Sensing
- Publication Date: Dec. 30, 2024
- Vol. 45, Issue 6, 82 (2024)
[in Chinese]
Predicting Impact Responses of the Spacecraft Soft Landing on the Airbag System by the Long Short-Term Memory Network
Xinyi SHEN, Kang YU, Jun YAN, and Caishan LIU
To realize the new vision of space exploration, the capacity of the new generation of manned spacecraft is significantly increased, which results in greater impact load during landing. The airbag cushioning system can substantially attenuate the impact acceleration but increases the difficulty of system design and analTo realize the new vision of space exploration, the capacity of the new generation of manned spacecraft is significantly increased, which results in greater impact load during landing. The airbag cushioning system can substantially attenuate the impact acceleration but increases the difficulty of system design and analysis. This paper utilizes the long short-term memory network and the dataset generated by finite element analysis to train a surrogate model for quickly predicting the impact acceleration of the spacecraft when soft landing on the complex airbag cushioning system. Comparison of the prediction results between the finite element analysis and the surrogate model shows that the LSTM-based surrogate model can quickly predict the impact acceleration under the body fixed coordinate system of the spacecraft, especially in the prediction of the longitudinal acceleration along the spacecraft's rotation axis and the transverse acceleration perpendicular to the rotation axis. The relative error between the surrogate model and the finite element analysis is about 10%, but the prediction speed is 100,000 times faster. It is fully proved that the proposed surrogate model can effectively accelerate the design cycle of such rigid-flexible coupled complex systems and improve the efficiency of engineering calculations..
Spacecraft Recovery & Remote Sensing
- Publication Date: Dec. 30, 2024
- Vol. 45, Issue 6, 15 (2024)
Design and Space Thermal Environment Analysis of Deorbiting Spheres
Yan XU, Xiaofeng YIN, and Tong WU
With the increasing threat of space debris to spacecraft, it is necessary to study the de-orbit technology at the end of spacecraft life. According to the requirement of deorbiting of orbital module in the current space environment, an inflatable deorbiting sphere device is designed and analyzed. The de-orbit modeling With the increasing threat of space debris to spacecraft, it is necessary to study the de-orbit technology at the end of spacecraft life. According to the requirement of deorbiting of orbital module in the current space environment, an inflatable deorbiting sphere device is designed and analyzed. The de-orbit modeling of orbital module - deorbiting sphere system is conducted, and the atmospheric drag force of the deorbiting sphere is analyzed. The deformation of the sphere under the atmospheric drag force and air pressure is analyzed based on nonlinear finite element method, and the size and design inflation pressure of the deorbit sphere are determined. The influence mechanism of space thermal environment is studied, and a thermodynamic coupling sequence analysis framework for space inflatable structures is established. The change of gas state in the inner cavity and its influence on the configuration of the deorbiting spheres are considered. The temperature field, thermal deformation, thermal stress response and the state parameters of the gas in the cavity are obtained during the flight. The results show that the orbital module can be removed from orbit within 3 months with a 7 m sphere, and the space thermal environment has a certain influence on the configuration of the orbital diameter and the pressure in the inner cavity. The design and analysis methods in this paper provide technical support for the development of the inflatable deorbiting spheres in future..
Spacecraft Recovery & Remote Sensing
- Publication Date: Dec. 30, 2024
- Vol. 45, Issue 6, 27 (2024)
The Study on Tensile Properties of Space Deployable Flexible Thermoplastic Composites
Hao WANG, Zepeng ZHOU, Jiangbo BAI, Sicheng GE... and Hao XU|Show fewer author(s)
Traditional space deployable structures predominantly utilize flexible thin-film materials, which suffer from issues such as low structural stiffness after deployment, surface wrinkles, and the need for pressure replenishment to maintain internal pressure. Compared to membrane materials, flexible thermoplastic compositTraditional space deployable structures predominantly utilize flexible thin-film materials, which suffer from issues such as low structural stiffness after deployment, surface wrinkles, and the need for pressure replenishment to maintain internal pressure. Compared to membrane materials, flexible thermoplastic composites exhibit higher stiffness and strength after molding, better surface quality, and the ability to maintain their shape solely through their inherent stiffness. These characteristics make them highly advantageous and promising for application in the field of space deployable structures. In this regard, the paper focuses on a specific aramid thermoplastic composite and studies its tensile mechanical properties. Firstly, tensile tests are performed on both aramid yarns and the thermoplastic aramid composite to determine the yarn's tensile modulus and strength, as well as the composite's tensile stress-strain curve. Observations indicated that there is a distinct nonlinear characteristic in the stress-strain curve. Subsequently, a unit cell finite element model of the composite is developed. This model employed a micro-mechanical finite element method to simulate the damage progression of the composite under tensile loading. Lastly, a comparison between the predicted stress-strain curve and the experimental results revealed good agreement, validating the accuracy of the simulation methodology, and the simulated damage process illuminated the tensile failure mechanism of the aramid thermoplastic composite. The research results can provide reference for the design and application of flexible thermoplastic composites in space deployable structures..
Spacecraft Recovery & Remote Sensing
- Publication Date: Dec. 30, 2024
- Vol. 45, Issue 6, 37 (2024)
[in Chinese]
Atmospheric Correction Research for Sentinel-2 Imagery for Typical Meadow-Type Lakes
Fei MENG, Jianfei FENG, Pingjie FU, Jiawei ZHANG, and Feiyong CHEN
Aiming at the problem that one remote sensing image atmospheric correction algorithm is difficult to be applied to the spectral correction of different types of lakes at the same time, we select Nansihu as the study area, collect Sentinel-2 images of the region from 2019 to 2022, collect the spectral data of spacious lAiming at the problem that one remote sensing image atmospheric correction algorithm is difficult to be applied to the spectral correction of different types of lakes at the same time, we select Nansihu as the study area, collect Sentinel-2 images of the region from 2019 to 2022, collect the spectral data of spacious lakes and lakes covered by aquatic vegetation during the same period. Based on the adaptive weighting algorithm, two new frameworks for atmospheric correction of multispectral images, the Adaptive Weighted Average Atmospheric Correction Algorithm (AWA-AC) and the Improved Adaptive Weighted Average Atmospheric Correction Algorithm (IAWA-AC), are constructed by taking advantage of the advantages of the three traditional atmospheric correction methods, namely, Acolite, Sen2Cor and C2RCC. The results of the atmospheric correction experiments on the Sentinel-2 image of Nansi Lake using each algorithm and the evaluation of the comparative accuracies show that the new framework of atmospheric correction is more effective than the single traditional algorithm, and the new framework of atmospheric correction is better than the single traditional algorithm in terms of coefficient of determination (R2), root mean square error (RMSE), and average unbiased relative error (AURE) of the measured and atmospherically corrected image spectra in the area during the time period of the study. The new framework proposed in this study has the maximum enhancement values of 79.75%, 71.55% and 70.43% for the three metrics, respectively, compared with the single traditional atmospheric correction algorithm. In the absence of measured spectral data to derive R2, atmospheric correction of remotely sensed images using the IAWA-AC algorithm constructed in this study is able to obtain better spectral fidelity..
Spacecraft Recovery & Remote Sensing
- Publication Date: Dec. 30, 2024
- Vol. 45, Issue 6, 96 (2024)
Landcover Classification Method for Multispectral Satellite Remote Sensing Imagery Based on Improved YOLOv5
Yong LIU, Weili YANG, Pengyu GUO, Lu CAO... and Weidong ZHAO|Show fewer author(s)
Deep learning-based algorithms of land cover classification for multispectral satellite remote sensing imagery classification typically utilize RGB band data while overlooking other bands such as NIR, and the networks’ feature extraction and application expansion capabilities need improvement. Regarding this issue, thiDeep learning-based algorithms of land cover classification for multispectral satellite remote sensing imagery classification typically utilize RGB band data while overlooking other bands such as NIR, and the networks’ feature extraction and application expansion capabilities need improvement. Regarding this issue, this paper proposes a land cover classification method multispectral satellite remote sensing imagery classification method based on the improved YOLOv5, called VN-YOLOv5-Seg. This method jointly utilizes RGB and NIR band data as inputs, adopts YOLOv5 object detection network as the backbone network, and employs the ProtoNet network as the segmentation head to convert object detection into pixel-level land cover classification tasks. The GID-15 dataset is used for experiments to verify the effectiveness of this method, with RGB band and RGB+NIR band as network inputs. Comparative analyses are conducted between VN-YOLOv5-Seg and other land cover classification networks. Experimental results demonstrate that by adding the NIR band to the RGB band, the mean Intersection over Union (mIoU) is improved by 2.5%. Compared to the FCN segmentation head, the mIoU is improved by 8.1%. Compared to PSPNet, DeepLabV3, and U-Net methods, the mIoU is increased by 2.6%, 1.2%, and 1.4% respectively. These results fully validate the effectiveness of the method and the necessity of introducing more band information for land cover classification..
Spacecraft Recovery & Remote Sensing
- Publication Date: Dec. 30, 2024
- Vol. 45, Issue 6, 113 (2024)
Multi-scale Attention Network for High-Precision Automatic Detection of Water Bodies in SAR Images
Xiujuan LIANG, Hongguang XIAO, Lifu CHEN, and Xiqian FAN
Synthetic Aperture Radar (SAR) system owns all-weather imaging characteristics and important application value in water body detection, but there still exists the problem of difficulty in extracting the features of fine tributary water bodies when extracting the information of multi-scale water bodies. The article propSynthetic Aperture Radar (SAR) system owns all-weather imaging characteristics and important application value in water body detection, but there still exists the problem of difficulty in extracting the features of fine tributary water bodies when extracting the information of multi-scale water bodies. The article proposes a network called Multi-scale Attention LinkNet (MATLinkNet). The network is mainly divided into two parts: encoder and decoder. In the initial block stage before the encoder, multiple small convolutions are used instead of the traditional 7×7 convolution, which can extract more delicate water body information. Subsequently, the Attentional Multi-Scale Pyramid (AMSP) module with the attentional mechanism is constructed in the encoding stage to enhance the learning of water body features at different scales and focus on the important features of the water body. Finally, skip connections are designed to chain the features of the encoder and decoder to compensate for the loss of spatial information caused by multiple downsampling in the encoding stage, which effectively improves the extraction accuracy of the water body and reduces the training time at the same time. Experiments are conducted on the self-made Sentinel-1 SAR image water body dataset, and the independent test results show that the highest values of water body extraction accuracy and intersection and concatenation ratio reach 90.73% and 81.95%, respectively, which are 6.81 and 5.27 percentage points higher than that of the original LinkNet network, verifying the network’s excellent performance in the task of segmentation of SAR water body images..
Spacecraft Recovery & Remote Sensing
- Publication Date: Dec. 30, 2024
- Vol. 45, Issue 6, 124 (2024)
A Fusion of Spectral Gradient and Machine Learning to Detect Megacity Land Cover Changes
Yiwen LU, Lishen MAO, Yichun XIE, Meiling ZHOU... and Jing XU|Show fewer author(s)
Algebra models and machine learning are commonly used remote sensing methods in traditional land cover change detection studies. However, these traditional methods often fail to achieve accurate change detection on complex landscape, especially in detecting areas where changes have occurred, where a significant number Algebra models and machine learning are commonly used remote sensing methods in traditional land cover change detection studies. However, these traditional methods often fail to achieve accurate change detection on complex landscape, especially in detecting areas where changes have occurred, where a significant number of missed detections tend to occur. To more precisely classify land cover types and detect land cover changes, this study integrated index models and machine learning, proposing a change detection algorithm that combines spectral gradient difference information with random forest classification technology. This modified spectral gradient difference (MSGD) change detection method inherits the advantage of spectral gradient difference (SGD) that com-presses noise information and the strong ability of big data analysis. More importantly, it optimizes the completeness of the SGD change information through the branching structure of the tree model, achieving the description of both change intensity and change direction attributes. The results indicate that the proposed MSGD method detects land cover change with an overall accuracy of 96.13%, an misdetection rate of 0.94% and a Kappa coefficient of 0.65. Compared with traditional methods such as change vector analysis (CVA) and the original SGD method, the MSGD model reduced the missed detection rate of land cover change detection results by more than 50% and improved the Kappa coefficient by more than 25%. Therefore, the MSGD change detection results are more accurate, and it has more potential in megacity land cover change detection application..
Spacecraft Recovery & Remote Sensing
- Publication Date: Dec. 30, 2024
- Vol. 45, Issue 6, 137 (2024)
High-Resolution Remote Sensing Image Building Extraction Method Based on MFF-DeeplabV3+
Siyan LIU, Chunyue WANG, Lu FU, and Ling LI
In order to achieve accurate extraction of buildings from high-resolution remote sensing images, an automated extraction method is proposed within the DeeplabV3+ framework. Firstly, SENet154 with SE module is selected as the backbone network to enhance the model's ability to extract image feature information. AfterIn order to achieve accurate extraction of buildings from high-resolution remote sensing images, an automated extraction method is proposed within the DeeplabV3+ framework. Firstly, SENet154 with SE module is selected as the backbone network to enhance the model's ability to extract image feature information. After the SENet154 network, the image can obtain 6 different feature maps. Then, the proposed Multi Feature Fusion Network (MFF) is used to fuse 5 low scale feature data. This process can fully utilize the advantages of different scale features in local detail and semantic information representation, achieve the comprehensive use of high and low scale features, and improve the segmentation accuracy of the model. Then, the highest scale and highest dimension feature maps are input into the ASPP module, and dilated convolution is used to expand the receptive field and enhance semantic features in depth. Finally, in the Decoder part, the fused features are combined with the multi-scale semantic information obtained from the ASPP module to obtain fine-grained extraction results of buildings. The proposed method is evaluated on publicly available high-resolution remote sensing building datasets through experiments on the effectiveness of the SE module, the combination of different backbone networks and multi-feature fusion, and comparisons with various commonly used methods. The method achieves precision, recall, and F1 scores of over 94% and an IoU score close to 90%. When using the proposed method, the accuracy and robustness of building extraction are significantly improved, and the performance is more outstanding..
Spacecraft Recovery & Remote Sensing
- Publication Date: Dec. 30, 2024
- Vol. 45, Issue 6, 151 (2024)
Flexible Deployable Structure Technology
Research Progress on Inflatable Lunar Habitats and Key Technologies
Haiyi CHI, Kang TAN, Shuai YUAN, Wente PAN... and Zhichao WANG|Show fewer author(s)
The inflatable lunar habitat, known for its high packaging efficiency, light weight, and low transportation costs, is considered one of the most promising designs for lunar habitation. The harsh lunar environment faced by lunar habitats is reviewed, and a classification of typical flexible inflatable lunar habitat desiThe inflatable lunar habitat, known for its high packaging efficiency, light weight, and low transportation costs, is considered one of the most promising designs for lunar habitation. The harsh lunar environment faced by lunar habitats is reviewed, and a classification of typical flexible inflatable lunar habitat design schemes is provided, both domestically and internationally. The advantages and disadvantages of different flexible inflatable lunar habitat construction plans are compared, and existed typical inflatable lunar habitat designs are summarized. Finally, based on the requirements for lunar habitats, key technologies for flexible lunar habitats are summarized, including thermal protection technology, micrometeoroid impact protection technology, and inflatable structure health monitoring technology. This paper offers a reference for the design of inflatable lunar habitats and construction of Chinese lunar base in future..
Spacecraft Recovery & Remote Sensing
- Publication Date: Dec. 30, 2024
- Vol. 45, Issue 6, 1 (2024)
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