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[in Chinese]
Research on the Impact Characteristics of Re-entry Capsule with Airbags under Wave Conditions
Jiapeng HAN, Shiqing WU, Yang ZHANG, Jiangli LEI, Wei HUANG, and Meifang ZHU
In order to study the water impact characteristics of the re-entry capsule with airbags under wave conditions, based on a combination model of a certain type of re-entry capsule and airbags, a multi-media coupling model of re-entry capsule-air bag-wave-air is established by using the fluid-structure coupling algorithm,In order to study the water impact characteristics of the re-entry capsule with airbags under wave conditions, based on a combination model of a certain type of re-entry capsule and airbags, a multi-media coupling model of re-entry capsule-air bag-wave-air is established by using the fluid-structure coupling algorithm, air bag simulation algorithm and wave making algorithm in LS-DYNA software to simulate the water landing process of the re-entry capsule with airbags under wave conditions. The effects of different entry angles and positions on the overload of re-entry capsule, the internal pressure of the airbags and the force of the airbags are discussed. The analysis shows that the larger the entry angle of the re-entry capsule with airbags, the smaller the overload. With the increase of the entry angle, the peak internal pressure of the airbags decreases. In addition, the water overload of the re-entry capsule with airbags at the crest of the wave is obviously less than that at the trough of the wave, and the force distribution of the airbag system shows obvious differences. The research results are expected to provide technical reference for the design of offshore recovery system and water impact test..
Spacecraft Recovery & Remote Sensing
- Publication Date: Oct. 30, 2024
- Vol. 45, Issue 5, 1 (2024)
Research on Flight Kinematics of the Parafoil System under Wind
Liang DONG, Yanjun LI, Yang CHENG, and Li YU
To investigate the wind effect on the flight kinematics of the parafoil system, a six-degree-of-freedom flight dynamics model considering wind interference is established. Runge Kuta method is applied to simulate the gliding performance of the parafoil system, and the numerical results of the parafoil flight kinematicsTo investigate the wind effect on the flight kinematics of the parafoil system, a six-degree-of-freedom flight dynamics model considering wind interference is established. Runge Kuta method is applied to simulate the gliding performance of the parafoil system, and the numerical results of the parafoil flight kinematics under wind are consistent with the literature qualitatively and quantitatively. On this basis, the flight kinematics of the parafoil system under wind of different directions at different flight altitudes is investigated numerically. The transient variations of velocity, trajectory and attitude angles are compared as well as those at steady state. The results showed that the wind changes the parafoil relative speed to air, leading to different aerodynamics angles and hence aerodynamic performance of the parafoil. As a result, the system dynamics equilibrium becomes unbalanced, and the speed as well as attitude changes rapidly until reaches steady state. The steady flight speed is slightly larger than the vector sum of original speed and wind speed. The system pitches down against the wind, pitches up in the wind, and system exhibits roll and yaw motions in the crosswind. Flight altitude affects the equilibrium speed of the system through the wind speed and air density. The higher the flight altitude, the larger the flight speed, however, the glide ratio and attitude angles are nearly constant. The flight characteristics pf parafoil system under wind in this paper can contribute to the flight trajectory planning and flight control..
Spacecraft Recovery & Remote Sensing
- Publication Date: Oct. 30, 2024
- Vol. 45, Issue 5, 13 (2024)
[in Chinese]
Design of the Infrared Optical System for Panoramic Girth Secondary Imaging
Yifan LIU, Feng ZHOU, Bin HU, and Libing JIN
The traditional panoramic ring optical system is composed of a single lens panoramic block and a relay lens system, without an intermediate image plane and mostly used in the visible light band. It can not limit the stray light outside the imaging field of view through the field of view diaphragm, and the light can reaThe traditional panoramic ring optical system is composed of a single lens panoramic block and a relay lens system, without an intermediate image plane and mostly used in the visible light band. It can not limit the stray light outside the imaging field of view through the field of view diaphragm, and the light can reach the image plane without reflection of the head unit or after multiple reflections in the head unit. At the same time, due to the particularity of infrared transmission materials, its application in the infrared spectrum will face a series of problems such as low transmittance and low refractive index temperature stability, which will affect the imaging quality. To solve these problems, the secondary imaging panoramic ring optical system with two mirrors as the head unit is adopted in this paper to create the conditions for the introduction of the aperture of the field of view for the suppression of stray light. Based on the aberration theory, the initial structural design method of the panoramic ring optical system with two-reflection lens unit is discussed. The Q-type aspherical surface profile description is introduced as an important variable in head element optimization. A panoramic band infrared optical system was designed with field of view (50°~70°) ×360°, focal length 7.5 mm, F-number 1.5 and working spectrum 8 ~10 μm. The design results show that the modulation transfer function of the system is better than 0.5 at the Nyquist frequency (20 lp line pair/mm), and the imaging quality is good. The stray light outside the designed field of view can be effectively suppressed to meet the needs of practical applications..
Spacecraft Recovery & Remote Sensing
- Publication Date: Oct. 30, 2024
- Vol. 45, Issue 5, 23 (2024)
Optimization and Practice of the Thermal Control System for Batch Production of Remote Sensing Satellites
Yuanbo ZHANG, Jian HUANG, Lin KONG, Tian BAI, Ming SHEN, Maosheng CHEN, and Jiwei ZOU
In response to the requirements for the development of mass production remote sensing satellites, an optimization strategy for the thermal control system was explored and applied to the development process of a certain batch of Jilin-1 satellites. Compared to the development process of single satellites, which focuses In response to the requirements for the development of mass production remote sensing satellites, an optimization strategy for the thermal control system was explored and applied to the development process of a certain batch of Jilin-1 satellites. Compared to the development process of single satellites, which focuses on target feasibility verification, the focus of mass production satellite development is on lower cost and more efficient production. The article follows the idea of integrating structure and thermal control, and conducts thermal control design with low external heat flux sensitivity, low temperature control power consumption, and low material cost; Relying on pulsating AIT production lines and automated production equipment, the thermal control implementation process is refined, thus shortening the preparation time, flexibly arranging personnel workstations, and significantly improving satellite production efficiency; Based on the thermal control design characteristics and experimental requirements of mass-produced satellites, an external heat flow simulation method based on equivalent heat sink is adopted to reduce the complexity of vacuum thermal testing. Currently, 54 satellites of this model have been produced and launched, and the satellites are good in orbit. This thermal control batch production development concept can provide an effective reference for the development of other batch production satellites..
Spacecraft Recovery & Remote Sensing
- Publication Date: Oct. 30, 2024
- Vol. 45, Issue 5, 31 (2024)
[in Chinese]
The RFM Iterative Adjustment Method for Optical Satellite Remote Sensing Imagery
Junpeng YU, Zilong ZHANG, and Xinyu LI
The RFM model for optical satellite remote sensing images is a major model for geometric processing of optical satellite remote sensing images, and its adjustment method has always been a focus of satellite photogrammetry research. This article proposes an RFM iterative adjustment method based on the image space adjustThe RFM model for optical satellite remote sensing images is a major model for geometric processing of optical satellite remote sensing images, and its adjustment method has always been a focus of satellite photogrammetry research. This article proposes an RFM iterative adjustment method based on the image space adjustment solution. Firstly, affine transformation polynomials are used to eliminate the image positioning error caused by internal and external orientation elements. Then, Fourier polynomials are continuously used to gradually eliminate complex image distortions. By combining both polynomials, the comprehensive compensation ability for eliminating image system errors is improved. The experimental results on two domestic satellite remote sensing images, ZY-3 and GF-7, show that the RFM positioning accuracy improves with the increase of iteration adjustment times, and the RMSE gradually converges after three times. Compared to the results of common adjustment solution, after iterative adjustment the RMSE of the plane and elevation positioning of ZY-3 images decreased by 8% and 13%, and the RMSE of the plane and elevation positioning of GF-7 images decreased by 12% and 30%. The RPC parameters generated after iterative adjustment can better meet the high-precision positioning requirements..
Spacecraft Recovery & Remote Sensing
- Publication Date: Oct. 30, 2024
- Vol. 45, Issue 5, 43 (2024)
A Multispectral and Panchromatic Image Fusion Algorithm Based on Particle Swarm Optimization and Pulse-Coupled Neural Network
Zhiwei ZHAO, Yukai FU, and Shuwen YANG
In order to further reduce the spectral and spatial distortion of the fused images from multispectral and panchromatic images, and improve the fusion quality, this paper proposes a particle swarm optimization pulse coupled neural network algorithm for multispectral and panchromatic image fusion. Based on the fusion fraIn order to further reduce the spectral and spatial distortion of the fused images from multispectral and panchromatic images, and improve the fusion quality, this paper proposes a particle swarm optimization pulse coupled neural network algorithm for multispectral and panchromatic image fusion. Based on the fusion framework of Principal Component Analysis and Non-Subsampled Contourlet Transform, the algorithm uses detail injection fusion method in low-frequency coefficient fusion process to reduce unnecessary information injection and improve spectral preservation. When fusing high-frequency coefficients, a simplified pulse coupled neural network with parameter adaptation is used to calculate the fusion weights, and the corresponding parameters that can obtain the best fusion quality are obtained by global search based on particle swarm optimization algorithm to improve the completeness and clarity of spatial information. The feasibility of the proposed algorithm is verified through three sets of experiments, and compared with existing and classic fusion algorithms. The experiments show that the proposed fusion algorithm has SAM around 0.1 and Q above 0.9 in all three sets of experiments. The experimental results show that the proposed algorithm can not only effectively improve the fusion quality of panchromatic and multispectral images, but also robust, and has the best fusion performance in comparison experiments..
Spacecraft Recovery & Remote Sensing
- Publication Date: Oct. 30, 2024
- Vol. 45, Issue 5, 51 (2024)
Hyperspectral Image Destriping Method Based on Nonlocal Low-Rank and Total Variation
Xiangyang KONG, Jiao ZHANG, Hui WANG, and Baogen XU
Due to factors such as uneven pixel response of the detector, mechanical motion of the sensor, and temperature changes during image acquisition, hyperspectral images often contain stripe noise. Current destriping methods often focus on the overall properties of the stripes and ignore their non-local similarity, making Due to factors such as uneven pixel response of the detector, mechanical motion of the sensor, and temperature changes during image acquisition, hyperspectral images often contain stripe noise. Current destriping methods often focus on the overall properties of the stripes and ignore their non-local similarity, making it difficult to achieve satisfactory destriping results. To address the above issues, this article proposes a destriping algorithm based on non-local low-rank tensor decomposition and total variation by analyzing the prior information of stripe noise and clean images. This algorithm considers the non-local similarity of stripes, clusters stripes similar to the reference block, and then approximates them using tensor low-rank decomposition. In addition, it also considers the directional and structural sparse characteristics of bandstripes, and achieves effective reduction of spectral distortion by jointly considering the local and non-local similarity of hyperspectral images. To evaluate the destriping effect of this method, we conducted both simulated data experiments and real data (Data from the Gaofen-5 satellite and data captured by the EO-1 Hyperion hyperspectral sensor of the Earth observation satellite in a certain region of Australia) experiments. The results of the simulated data experiments showed that under random length stripes and overall stripes, the mean peak signal-to-noise ratio (MPSNR) and mean structural similarity index measure (MSSIM) values of this algorithm were about 2~3 dB and 0.02~0.04 higher than the best results in the comparison method, respectively, while the mean spectral angle mapper (MSAM) value decreased by about 0.02~0.06. The results of real data experiments show that the algorithm can accurately estimate and separate stripes, recover image information affected by stripes, overcome the problem of residual stripes, and outperform the comparison method in terms of the inverse coefficient of variation (ICV) and mean relative deviation (MRD), which are non-reference evaluation metrics.The algorithm proposed in this article provides an effective solution for removing stripe noise in hyperspectral images, and is expected to provide strong support for the subsequent applications of hyperspectral images..
Spacecraft Recovery & Remote Sensing
- Publication Date: Oct. 30, 2024
- Vol. 45, Issue 5, 64 (2024)
Infrared Dim Target Detection Based on Time-Space Domain Feature Fusion
Shuwei CUI, and Wenbo WU
Infrared target detection faces challenges such as limited effective pixels, low SNR, and difficulty in distinguishing targets from background and noise in the spatial domain. In response, we propose a infrared target detection method based on a spatiotemporal feature extraction module and an improved YOLOv5 object detInfrared target detection faces challenges such as limited effective pixels, low SNR, and difficulty in distinguishing targets from background and noise in the spatial domain. In response, we propose a infrared target detection method based on a spatiotemporal feature extraction module and an improved YOLOv5 object detection network. This method utilizes a three-dimensional residual structure to construct a space-time domain feature extraction module, enabling efficient extraction of space-time domain features of dim infrared targets and reducing interference from spatial domain noise in infrared image target detection. Additionally, we introduce the Coordinate Attention (CA) mechanism into the YOLOv5 convolutional neural network to address the challenge of detecting extremely weak targets relative to the background in weak target detection and improve the detection capability for weak targets. Experimental results demonstrate that compared to the YOLOv5s network, our proposed algorithm achieves a 2.2% increase in precision, a 2.1% improvement in recall, and a 3.5% increase in mean average precision at intersection over union 0.5. These results validate that the space-time domain feature fusion method can enhance the detection accuracy of weak infrared moving targets..
Spacecraft Recovery & Remote Sensing
- Publication Date: Oct. 30, 2024
- Vol. 45, Issue 5, 79 (2024)
Remote Sensing Image Change Detection Based on Scale-Aware and Spatial Selection Hierarchical Interaction
Pan SHAO, Zongsheng GUAN, Weiqi FU, Fanyu ZENG, Zemin CHENG, and Weichao SHI
Currently, remote sensing image change detection methods based on deep learning are still not effective enough to be satisfactory when dealing with images with significant scale changes, and most of the methods lack effective interactions between different layers of features in the decoding stage. Aiming at the above pCurrently, remote sensing image change detection methods based on deep learning are still not effective enough to be satisfactory when dealing with images with significant scale changes, and most of the methods lack effective interactions between different layers of features in the decoding stage. Aiming at the above problems, the paper proposes a high-resolution remote sensing image change detection method based on scale-aware and spatial selection hierarchical interaction in view of the classical U-net network. Firstly, a scale-aware module is designed by introducing channel attention after extracting features through chunked parallel depthwise separable convolutions of different sizes, in order to efficiently extract changing objects with different shape scales. Then, by utilizing spatial attention cross-enhancement between shallow and deep features, a spatial selection hierarchical interaction module is presented to refine the representational capabilities of the features. Finally, based on the difference maps of the two remote sensing images, a difference multi-scale attention module is given to highlight the changed information and suppress the unchanged information. The method proposed in the paper achieves F1 scores (the harmonic mean of precision and recall) of 91.72%, 85.17%, 90.82%, and 88.03% on four public datasets: WHU, Google, LEVIR, and GVLM, respectively. Compared to existing six change detection networks such as FC-EF, FC-Conc, IFN, SNUNet, BIT, and MSCANet, the F1 score is significantly improved..
Spacecraft Recovery & Remote Sensing
- Publication Date: Oct. 30, 2024
- Vol. 45, Issue 5, 89 (2024)
Study on Remote Sensing Estimation of Regional Evapotranspiration Based on GF-5B VIMI Data and the SEBS Model
Lijuan ZHANG, Haiyong DING, Chao ZHENG, Jiayu LIN, and Lingfeng YIN
Evapotranspiration is a key component of the global water cycle and significantly impacts water cycling and energy balance. However, existing evapotranspiration data exhibit limitations in their spatial and temporal resolution. To address this issue, this paper employs a new method that combines high-resolution GF-5B VEvapotranspiration is a key component of the global water cycle and significantly impacts water cycling and energy balance. However, existing evapotranspiration data exhibit limitations in their spatial and temporal resolution. To address this issue, this paper employs a new method that combines high-resolution GF-5B VIMI data with the SEBS model. The Dadukou District of Chongqing was selected as the study area. Using GF-5B VIMI imagery data and the ERA5-land climate reanalysis dataset, the daily evapotranspiration of the area was estimated, and its spatial and temporal distribution characteristics, as well as the evapotranspiration from different underlying surfaces, were analyzed. Additionally, the relationship between evapotranspiration and the Normalized Difference Vegetation Index (NDVI) was examined. Experimental results indicate that the estimates from the SEBS model are in good agreement with observations from eddy covariance systems, as evidenced by a determination coefficient (R²) of 0.764 and a root mean square error (RMSE) of 0.348 mm/d. Among the different land use types, water bodies showed the highest daily evapotranspiration rates, while bare lands showed the lowest. The correlation coefficient (R) between daily evapotranspiration and NDVI was 0.908, with a determination coefficient (R²) of 0.824, further validating the model's effectiveness and applicability. This approach significantly enhances the spatiotemporal resolution of evapotranspiration and offers critical scientific insights for the efficient management of water resources..
Spacecraft Recovery & Remote Sensing
- Publication Date: Oct. 30, 2024
- Vol. 45, Issue 5, 101 (2024)
Research on Automatic Detection of Terrain Occlusion Areas in Satellite Oblique Images
Zongqi LIU, Zhanliang YUAN, Donghong WANG, Xingfeng CHEN, Jun LIU, Lei ZHANG, Bolun CUI, and Limin ZHAO
In addressing the prevalent issue of terrain occlusion in satellite oblique images, this paper proposes a high-resolution occlusion detection method based on the Rational Polynomial Coefficients (RPC) model. The proposed approach utilizes an irregular triangular mesh to represent the three-dimensional terrain surface aIn addressing the prevalent issue of terrain occlusion in satellite oblique images, this paper proposes a high-resolution occlusion detection method based on the Rational Polynomial Coefficients (RPC) model. The proposed approach utilizes an irregular triangular mesh to represent the three-dimensional terrain surface and employs ray tracing techniques to accurately detect the intersections of incident rays with this mesh. This process facilitates the determination of occlusion relationships among the intersection points. To enhance the efficiency of occlusion detection, a strategy is developed that minimizes the search area within the terrain. Experimental validation is performed using both real oblique satellite images and simulated satellite images. The results demonstrate that the proposed method achieves an occlusion detection accuracy exceeding 97%, the area affected by terrain occluded area can be identified effectively and the mask image of terrain occluded area can be generated automatically..
Spacecraft Recovery & Remote Sensing
- Publication Date: Oct. 30, 2024
- Vol. 45, Issue 5, 112 (2024)
Research on Area Correction for Monitoring Cyanobacterial Bloom with Medium and Low Resolution Satellites: Taking GOCI-2 as an Example
Yaping WANG, Xifei XU, Jiaguo LI, Xingfeng CHEN, Ning ZHANG, Huajie CHEN, Limin ZHAO, and Jun LIU
In the face of the problem that the accuracy of cyanobacteria bloom area in lakes estimated with low and medium resolution images is poor due to the low monitoring scale, this paper took GOCI-2 as an example, selected the Taihu Lake as research area, compared the difference in cyanobacteria bloom area extracted from GOIn the face of the problem that the accuracy of cyanobacteria bloom area in lakes estimated with low and medium resolution images is poor due to the low monitoring scale, this paper took GOCI-2 as an example, selected the Taihu Lake as research area, compared the difference in cyanobacteria bloom area extracted from GOCI-2 and Sentinel-2, researched the relationship between the NDVI of GOCI-2 and the proportion of cyanobacteria bloom area in mixed pixels and conducted regression analysis. Based on this, a corrected model of the cyanobacteria bloom area for GOCI-2 was established. The accuracy of the model was compared and analyzed. The results showed that: a non-linear positive correlation was found between the proportion of cyanobacterial blooms area and NDVI value in mixed pixels, and the area of cyanobacterial blooms extracted directly using the NDVI threshold method was larger than the actual value. After correction by this model, the average accuracy of GOCI-2 cyanobacterial blooms area monitoring was improved from 67.8% to 90.0%. And the model in this article was not sensitive to the changes in NDVI threshold settings. NDVI was found to better reflect the proportion of cyanobacterial blooms in mixed pixels, compared with the algorithm of EVI and AFAI which were used to extract cyanobacterial bloom. The research results of this paper can provide valuable reference for the application of GOCI-2 image in the field of cyanobacteria bloom monitoring..
Spacecraft Recovery & Remote Sensing
- Publication Date: Oct. 30, 2024
- Vol. 45, Issue 5, 123 (2024)
Application Evaluation of Vegetation Ecological Survey in Plateau Area of ZY-1 02E Satellite
Guanbing HU, Zhijian LIAO, Fang LIU, Wei DANG, and Kun YANG
In order to analyze and evaluate the application ability of multi-spectral and hyper-spectral data of ZY-1 02E satellite in vegetation ecological investigation in plateau area, this paper chooses Huize-Dongchuan in northeast Yunnan as the research area, which is characterized by obvious vertical zoning of vegetation, BIn order to analyze and evaluate the application ability of multi-spectral and hyper-spectral data of ZY-1 02E satellite in vegetation ecological investigation in plateau area, this paper chooses Huize-Dongchuan in northeast Yunnan as the research area, which is characterized by obvious vertical zoning of vegetation, By using multi-spectral data, visual interpretation and vegetation index extraction were carried out respectively, and data such as GF-1 and Sentinel-2 with similar spatial resolution and imaging time were selected to compare and analyze the results. In the plateau area, support vector machine method, neural network method and spectral angle classification method were used to analyze the vertical zoning characteristics of mountain vegetation and extract typical vegetation types. Combined with the field measured spectrum and forestry survey data, evaluate the application effect of hyperspectral. The results show that the multi-spectral data of ZY-1 02E satellite has clear image features of main vegetation types and is easy to distinguish and identify. The extraction results of vegetation index are in good agreement with the comparison data. Hyperspectral data can quickly extract different vegetation ecological types, and the overall accuracy can reach more than 90%. The ZY-1 02E satellite data can be well applied to the investigation and fine classification of vegetation ecosystems in the plateau area, and has good regional ecosystem monitoring capabilities..
Spacecraft Recovery & Remote Sensing
- Publication Date: Oct. 30, 2024
- Vol. 45, Issue 5, 134 (2024)
Real-Time Fire Detection by Cascading Traditional Approaches with Deep Learning
Wenzhuo WANG, Chenglong MA, Guanlin WANG, Yiming ZHANG, Fangxiong TAN, Xu HAN, and Lei WU
Aiming at the problem of insufficient accuracy and timeliness of wildfire monitoring, a near real-time fire monitoring algorithm using a Multichannel Convolutional Neural Network (MCNN) with a cascaded traditional method is proposed. Firstly, by combining the OTSU method and the spatial context method, potential fire pAiming at the problem of insufficient accuracy and timeliness of wildfire monitoring, a near real-time fire monitoring algorithm using a Multichannel Convolutional Neural Network (MCNN) with a cascaded traditional method is proposed. Firstly, by combining the OTSU method and the spatial context method, potential fire points are identified by exploiting the differences in background brightness temperature spatial information. Secondly, using the idea of ensemble learning, three convolutional neural network channels are constructed. Each channel takes different combinations of spectral information, spatial context information, and temporal-geographical information features as input. The optimal weights for each channel are obtained by using the particle swarm optimization algorithm to search for the best weights, and the joint prediction probabilities of fire points from the three channels are obtained, achieving accurate fire point recognition. The results show that compared to a single-channel Convolutional Neural Network (CNN) model, the MCNN achieves a precision of 0.88 and reduces the omission rate by 0.16. Furthermore, compared to the Japan Meteorological Agency’s official product, the omission rate is reduced by 0.06. In addition, the highest runtime of the model in the experiment is 268 seconds. Therefore, the MCNN model proposed in this paper can achieve high-precision near real-time fire point detection, providing a scientific basis for emergency fire response..
Spacecraft Recovery & Remote Sensing
- Publication Date: Oct. 30, 2024
- Vol. 45, Issue 5, 147 (2024)