Pub Date : 2019-11-01DOI: 10.1109/APSAR46974.2019.9048408
C. Yang, Hui Yu, Long Zhuang, M. Hao
Deriving characteristic parameters is very important to the accurate interpretation of synthetic aperture radar (SAR) image and the application of land cover classification. In this paper, we apply the uniform polarimetric matrix rotation theory to the polarimetric interferometric SAR (PolInSAR) data and deduce the parameter set of the polarimetric interferometric coherency matrix in rotation domain. The relationship between the characteristics of the parameter set and the terrain is also analyzed. Finally, we propose a land cover classification scheme using parameters in rotation domain and apply it to measured PolInSAR data. The classification result is better than using the parameters in rotation domain of polarimetric SAR (PolSAR) data and confirm that the polarimetric interferometric coherency matrix parameters in rotation domain can be used for land cover classification.
{"title":"Study on Characteristics of Polarimetric Interferometric SAR Data in Rotation Domain","authors":"C. Yang, Hui Yu, Long Zhuang, M. Hao","doi":"10.1109/APSAR46974.2019.9048408","DOIUrl":"https://doi.org/10.1109/APSAR46974.2019.9048408","url":null,"abstract":"Deriving characteristic parameters is very important to the accurate interpretation of synthetic aperture radar (SAR) image and the application of land cover classification. In this paper, we apply the uniform polarimetric matrix rotation theory to the polarimetric interferometric SAR (PolInSAR) data and deduce the parameter set of the polarimetric interferometric coherency matrix in rotation domain. The relationship between the characteristics of the parameter set and the terrain is also analyzed. Finally, we propose a land cover classification scheme using parameters in rotation domain and apply it to measured PolInSAR data. The classification result is better than using the parameters in rotation domain of polarimetric SAR (PolSAR) data and confirm that the polarimetric interferometric coherency matrix parameters in rotation domain can be used for land cover classification.","PeriodicalId":377019,"journal":{"name":"2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121812601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/APSAR46974.2019.9048264
Chen Zhao, Pengbo Wang, Jian Wang, Zhirong Men
Recently, with the development of deep learning and the springing up of synthetic aperture radar (SAR) images, SAR maritime target detection based on convolutional neural network (CNN) has become a hot issue. However, most related work is realized on general purpose hardware like CPU or GPU, which is energy consuming, non-real-time and unable to be deployed on embedded devices. Aiming at this problem, this paper proposes a method to deploy a model of SAR maritime target detection network on an embedded device which employs custom artificial intelligence streaming architecture (CAISA). Moreover, the model is trained and tested on the Gaofen-3 (GF-3) spaceborne SAR images, which include six different kinds of maritime targets. Experiments based on the GF-3 dataset show the method is practicable and extensible.
{"title":"A Maritime Target Detector Based on CNN and Embedded Device for GF-3 Images","authors":"Chen Zhao, Pengbo Wang, Jian Wang, Zhirong Men","doi":"10.1109/APSAR46974.2019.9048264","DOIUrl":"https://doi.org/10.1109/APSAR46974.2019.9048264","url":null,"abstract":"Recently, with the development of deep learning and the springing up of synthetic aperture radar (SAR) images, SAR maritime target detection based on convolutional neural network (CNN) has become a hot issue. However, most related work is realized on general purpose hardware like CPU or GPU, which is energy consuming, non-real-time and unable to be deployed on embedded devices. Aiming at this problem, this paper proposes a method to deploy a model of SAR maritime target detection network on an embedded device which employs custom artificial intelligence streaming architecture (CAISA). Moreover, the model is trained and tested on the Gaofen-3 (GF-3) spaceborne SAR images, which include six different kinds of maritime targets. Experiments based on the GF-3 dataset show the method is practicable and extensible.","PeriodicalId":377019,"journal":{"name":"2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127087463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/APSAR46974.2019.9048349
Yue Wang, B. Su, Lei Huang
Many indoor localization and navigation systems require the information of the wall layout. The paper proposes a 3-D wall mapping algorithm using arbitrarily placed nodes. The proposed algorithm estimates the normal vector of the reflector surface and the scaling factor of the translation vector. The constrained CRLB of the reflector parameters is derived. The performance of the wall mapping algorithm is examined by comparing the MSE with the constrained Cramer-Rao lower bound (CRLB).
{"title":"3-D Reflector Mapping Using Sensor Network","authors":"Yue Wang, B. Su, Lei Huang","doi":"10.1109/APSAR46974.2019.9048349","DOIUrl":"https://doi.org/10.1109/APSAR46974.2019.9048349","url":null,"abstract":"Many indoor localization and navigation systems require the information of the wall layout. The paper proposes a 3-D wall mapping algorithm using arbitrarily placed nodes. The proposed algorithm estimates the normal vector of the reflector surface and the scaling factor of the translation vector. The constrained CRLB of the reflector parameters is derived. The performance of the wall mapping algorithm is examined by comparing the MSE with the constrained Cramer-Rao lower bound (CRLB).","PeriodicalId":377019,"journal":{"name":"2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126167993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/APSAR46974.2019.9048469
Song Chen, Zhou Liangjiang, W. Shuai, Wu Yirong, D. Chibiao
The micro-Doppler modulation caused by the rotor on the unmanned aerial vehicle(UAV) can be employed to recognize UAV. However, it will be a challenging issue when serveral rotors on a UAV and multiple targets in the same scene, which directly leads to multicomponent micro-Doppler existing in the radar echo. In this paper, we firstly proposed a novel algorithm for multicomponent micro-Doppler signal decomposition and parameter estimation. The algorithm mainly contains a signal decomposition method utilizing Hough transform and a parameter estimation method based on matching kernel function. When appropriate parameters are chosen to match with the time-frequency characteristics of a certain component the energy distribution of the time-frequency representation is significantly improved and the energy distribution of other components is more dispersed. We have verified the algorithm with simulation and actual experiments, the result shows that the algorithm works well and effectively.
{"title":"A Signal Decomposition and Parameter Estimation Method for Multi-Rotor UAV","authors":"Song Chen, Zhou Liangjiang, W. Shuai, Wu Yirong, D. Chibiao","doi":"10.1109/APSAR46974.2019.9048469","DOIUrl":"https://doi.org/10.1109/APSAR46974.2019.9048469","url":null,"abstract":"The micro-Doppler modulation caused by the rotor on the unmanned aerial vehicle(UAV) can be employed to recognize UAV. However, it will be a challenging issue when serveral rotors on a UAV and multiple targets in the same scene, which directly leads to multicomponent micro-Doppler existing in the radar echo. In this paper, we firstly proposed a novel algorithm for multicomponent micro-Doppler signal decomposition and parameter estimation. The algorithm mainly contains a signal decomposition method utilizing Hough transform and a parameter estimation method based on matching kernel function. When appropriate parameters are chosen to match with the time-frequency characteristics of a certain component the energy distribution of the time-frequency representation is significantly improved and the energy distribution of other components is more dispersed. We have verified the algorithm with simulation and actual experiments, the result shows that the algorithm works well and effectively.","PeriodicalId":377019,"journal":{"name":"2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123678107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/APSAR46974.2019.9048502
Qian Guo, Haipeng Wang, F. Xu
Accurate aircraft detection in high-resolution Synthetic Aperture Radar (SAR) images is of great significance. Aiming at the challenges of sparsity and variability for aircraft targets in SAR images, a detection algorithm based on Scattering Feature Information (SFI) enhancement and Feature Pyramid Network (FPN) is proposed. In the former stage, the SFI, being composed of Strong Scattering Point (SSP) and its corresponding scattering region distribution model, is extracted by Harris-Laplace detector and Gaussian Mixture Model (GMM). Specially, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is introduced to response to the sensitivity of the GMM to the initial values. In the detection stage, an algorithm based on FPN is applied for aircraft detection in high-resolution images. This structure combines the high-resolution information of the underlying features with the high-semantic information of the deep features, which facilitates accurate detection of the aircrafts in a scene. In addition, Logarithmic-normal Distribution based Subdivided Conversion (LDSC) is newly proposed for SAR image preprocessing. Experiments conducted on the GF-3 satellite image of 0.5 m resolution demonstrates the superiority and robustness of the proposed method.
{"title":"Aircraft Detection in High-Resolution SAR Images Using Scattering Feature Information","authors":"Qian Guo, Haipeng Wang, F. Xu","doi":"10.1109/APSAR46974.2019.9048502","DOIUrl":"https://doi.org/10.1109/APSAR46974.2019.9048502","url":null,"abstract":"Accurate aircraft detection in high-resolution Synthetic Aperture Radar (SAR) images is of great significance. Aiming at the challenges of sparsity and variability for aircraft targets in SAR images, a detection algorithm based on Scattering Feature Information (SFI) enhancement and Feature Pyramid Network (FPN) is proposed. In the former stage, the SFI, being composed of Strong Scattering Point (SSP) and its corresponding scattering region distribution model, is extracted by Harris-Laplace detector and Gaussian Mixture Model (GMM). Specially, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is introduced to response to the sensitivity of the GMM to the initial values. In the detection stage, an algorithm based on FPN is applied for aircraft detection in high-resolution images. This structure combines the high-resolution information of the underlying features with the high-semantic information of the deep features, which facilitates accurate detection of the aircrafts in a scene. In addition, Logarithmic-normal Distribution based Subdivided Conversion (LDSC) is newly proposed for SAR image preprocessing. Experiments conducted on the GF-3 satellite image of 0.5 m resolution demonstrates the superiority and robustness of the proposed method.","PeriodicalId":377019,"journal":{"name":"2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125323072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/APSAR46974.2019.9048500
He Huang, Xiaoping Li, Yanming Liu
In consideration of the application background of deep space detection and data transmission, this paper proposes a dual-polarization antenna working at X/Ku/Ka band. Three elements resonating at different frequency points are innovatively interleaved, covering the usually used bands of TT&C (Tracing, Telemetry and Command) and Data Transmission and sharing with the same aperture. The antenna volume is only $65text{mm}times 65text{mm}times 1.3text{mm}$. Compared with the traditional array, the proposed antenna has the advantages of low-profile and high integration. The antenna has good performance over the working bands, which has potential in the synthetic aperture radar applications.
考虑到深空探测和数据传输的应用背景，本文提出了一种工作在X/Ku/Ka波段的双极化天线。三种不同频率点共振的元素创新性地交织在一起，覆盖了TT&C (tracking, Telemetry and Command)和Data Transmission常用的频段，共用同一个孔径。天线体积仅为$65text{mm}乘以65text{mm}乘以1.3text{mm}$。与传统阵列相比，该天线具有外形小、集成度高的优点。该天线在工作频带内具有良好的性能，在合成孔径雷达中具有应用潜力。
{"title":"Tri-band, Dual-polarized Antenna with Shared Aperture for TT&C and Data Transmission","authors":"He Huang, Xiaoping Li, Yanming Liu","doi":"10.1109/APSAR46974.2019.9048500","DOIUrl":"https://doi.org/10.1109/APSAR46974.2019.9048500","url":null,"abstract":"In consideration of the application background of deep space detection and data transmission, this paper proposes a dual-polarization antenna working at X/Ku/Ka band. Three elements resonating at different frequency points are innovatively interleaved, covering the usually used bands of TT&C (Tracing, Telemetry and Command) and Data Transmission and sharing with the same aperture. The antenna volume is only $65text{mm}times 65text{mm}times 1.3text{mm}$. Compared with the traditional array, the proposed antenna has the advantages of low-profile and high integration. The antenna has good performance over the working bands, which has potential in the synthetic aperture radar applications.","PeriodicalId":377019,"journal":{"name":"2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126751689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/APSAR46974.2019.9048543
Rui Liu, Daiyin Zhu, Die Wang, Wanwan Du
The miniaturized synthetic aperture radar (MiniSAR) signal processing system is designed and implemented in this paper, which is able to deal with chirped SAR signals based on FPGA. In this design, the Polar Format Algorithm (PFA) using the principle of chirp scaling (PCS) for range processing and Sinc interpolation for azimuth processing can achieve high precision results and increase speed significantly. Meanwhile, the phase gradient autofocus (PGA) and the geometric correction (GC) are applied to estimate and compensate for the residual phase error accurately and realize wavefront curvature correction caused by PFA. The system uses the Floating-Point IP cores and pipeline structure to achieve high-speed floating-point data computation, and uses a smart scheme to realize the transposition of matrix data demanded by the system algorithm with DDR3 SDRAM. The system is built on Virtex7-XC7VX690T evaluation board, and it takes 2.1s to obtain 4K*2K complex-image in single precision. Point target simulation has validated the presented methodology, and the real data processing results verify the reliability and stability of the proposed system.
本文设计并实现了基于FPGA的小型合成孔径雷达(MiniSAR)信号处理系统，该系统能够处理啁啾SAR信号。在本设计中，利用啁啾缩放(PCS)原理进行距离处理，利用Sinc插值原理进行方位角处理的极坐标格式算法(Polar Format Algorithm, PFA)可以获得高精度结果，并显著提高速度。同时，采用相位梯度自动聚焦(PGA)和几何校正(GC)对残差进行精确估计和补偿，实现了由相位梯度自动聚焦引起的波前曲率校正。系统采用浮点IP核和流水线结构实现高速浮点数据计算，并采用智能方案用DDR3 SDRAM实现系统算法所需的矩阵数据的转置。系统基于Virtex7-XC7VX690T评估板，单精度获取4K*2K复杂图像耗时2.1s。点目标仿真验证了所提方法的有效性，实际数据处理结果验证了所提系统的可靠性和稳定性。
{"title":"FPGA Implementation of SAR Imaging Processing System","authors":"Rui Liu, Daiyin Zhu, Die Wang, Wanwan Du","doi":"10.1109/APSAR46974.2019.9048543","DOIUrl":"https://doi.org/10.1109/APSAR46974.2019.9048543","url":null,"abstract":"The miniaturized synthetic aperture radar (MiniSAR) signal processing system is designed and implemented in this paper, which is able to deal with chirped SAR signals based on FPGA. In this design, the Polar Format Algorithm (PFA) using the principle of chirp scaling (PCS) for range processing and Sinc interpolation for azimuth processing can achieve high precision results and increase speed significantly. Meanwhile, the phase gradient autofocus (PGA) and the geometric correction (GC) are applied to estimate and compensate for the residual phase error accurately and realize wavefront curvature correction caused by PFA. The system uses the Floating-Point IP cores and pipeline structure to achieve high-speed floating-point data computation, and uses a smart scheme to realize the transposition of matrix data demanded by the system algorithm with DDR3 SDRAM. The system is built on Virtex7-XC7VX690T evaluation board, and it takes 2.1s to obtain 4K*2K complex-image in single precision. Point target simulation has validated the presented methodology, and the real data processing results verify the reliability and stability of the proposed system.","PeriodicalId":377019,"journal":{"name":"2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"237 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114299680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/APSAR46974.2019.9048281
Minmin Lan, Shi-hong Du, Kaizhi Wang
In this paper, we present a novel imaging strategy of SAR for ship detection. We firstly divide surveillance area into many adjacent cells. By introducing Bayesian approach, the target occurrence probability of each cell is obtained. Based on the probability, a target-searching strategy is proposed and interest cells are determined for SAR imaging. This imaging strategy only images regions where target-presence probability is high. Reduction of imaging in no-target areas promises this novel imaging strategy more efficient and low time cost.
{"title":"A Novel Imaging Strategy of SAR for Ship Detection","authors":"Minmin Lan, Shi-hong Du, Kaizhi Wang","doi":"10.1109/APSAR46974.2019.9048281","DOIUrl":"https://doi.org/10.1109/APSAR46974.2019.9048281","url":null,"abstract":"In this paper, we present a novel imaging strategy of SAR for ship detection. We firstly divide surveillance area into many adjacent cells. By introducing Bayesian approach, the target occurrence probability of each cell is obtained. Based on the probability, a target-searching strategy is proposed and interest cells are determined for SAR imaging. This imaging strategy only images regions where target-presence probability is high. Reduction of imaging in no-target areas promises this novel imaging strategy more efficient and low time cost.","PeriodicalId":377019,"journal":{"name":"2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114471228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/APSAR46974.2019.9048383
Pucheng Li, Huijuan Li, Lei Yang
In this paper, we propose a novel method called overlapping group Lasso to solve inverse synthetic aperture radar (ISAR) imaging problem. Unlike the traditional least absolute shrinkage and selection operator (Lasso) model, overlapping group Lasso is based on the $ell_{1}/ell_{2}$ mixed-norm and take advantage of the prior knowledge of the continuity structures of the scatters. Besides, we present a generic optimization approach, the alternating direction method of multipliers (ADMM) method, for dealing with overlapping group Lasso that including structured-sparsity penalties and the predefined weight for group. ADMM is a simple but powerful algorithm that blending the benefits of augmented Lagrangian and dual decomposition method. Therefore, it makes the proposed algorithm faster and more robust. Experimental results of simulated data and Yak-42 real data verify the feasibility of ADMM achieves sparse and structural feature enhancement via the overlapping group Lasso. The comparison of the results of overlapping group Lasso and Lasso shows: the new developed model has the good ability of denoising and structural feature enhancement.
{"title":"Overlapping-Group-Lasso-Based ISAR Imagery via Alternating Direction Method of Multipliers","authors":"Pucheng Li, Huijuan Li, Lei Yang","doi":"10.1109/APSAR46974.2019.9048383","DOIUrl":"https://doi.org/10.1109/APSAR46974.2019.9048383","url":null,"abstract":"In this paper, we propose a novel method called overlapping group Lasso to solve inverse synthetic aperture radar (ISAR) imaging problem. Unlike the traditional least absolute shrinkage and selection operator (Lasso) model, overlapping group Lasso is based on the $ell_{1}/ell_{2}$ mixed-norm and take advantage of the prior knowledge of the continuity structures of the scatters. Besides, we present a generic optimization approach, the alternating direction method of multipliers (ADMM) method, for dealing with overlapping group Lasso that including structured-sparsity penalties and the predefined weight for group. ADMM is a simple but powerful algorithm that blending the benefits of augmented Lagrangian and dual decomposition method. Therefore, it makes the proposed algorithm faster and more robust. Experimental results of simulated data and Yak-42 real data verify the feasibility of ADMM achieves sparse and structural feature enhancement via the overlapping group Lasso. The comparison of the results of overlapping group Lasso and Lasso shows: the new developed model has the good ability of denoising and structural feature enhancement.","PeriodicalId":377019,"journal":{"name":"2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128627868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/APSAR46974.2019.9048453
Fengming Hu, Jicang Wu
Synthetic aperture radar (SAR) images are able to detect changes in an urban area with short revisited time. Most presented change detection methods based on SAR images are conducted using couples of the images. However, the change detection results are not reliable since the amplitude observations are sensitive to the change of surroundings and the types of changes are unknown. Here we propose a new change detection method using an adaptive temporal subset multi-temporal InSAR method. Single pixel change detection is developed using amplitude time series in order to identify the step-times: changes in the temporal domain. Then the parameters, e.g. deformation velocity are estimated and the coherent intervals are determined using interferometric phase time series. With the identified TCS, we distinguish different types of changes based on their coherent intervals. The main advantages of our method are reliable unsupervised change detection and detecting different types of changes without additional information. Experimental results by proposed method show that both appearing and disappearing buildings with their step-times are successfully identified and results by COSMO-Skyed ascending and descending images show a good agreement.
{"title":"Spatial-temporal Change Detection in Urban Area Using Adaptive Temporal Subset Multi-temporal InSAR Method","authors":"Fengming Hu, Jicang Wu","doi":"10.1109/APSAR46974.2019.9048453","DOIUrl":"https://doi.org/10.1109/APSAR46974.2019.9048453","url":null,"abstract":"Synthetic aperture radar (SAR) images are able to detect changes in an urban area with short revisited time. Most presented change detection methods based on SAR images are conducted using couples of the images. However, the change detection results are not reliable since the amplitude observations are sensitive to the change of surroundings and the types of changes are unknown. Here we propose a new change detection method using an adaptive temporal subset multi-temporal InSAR method. Single pixel change detection is developed using amplitude time series in order to identify the step-times: changes in the temporal domain. Then the parameters, e.g. deformation velocity are estimated and the coherent intervals are determined using interferometric phase time series. With the identified TCS, we distinguish different types of changes based on their coherent intervals. The main advantages of our method are reliable unsupervised change detection and detecting different types of changes without additional information. Experimental results by proposed method show that both appearing and disappearing buildings with their step-times are successfully identified and results by COSMO-Skyed ascending and descending images show a good agreement.","PeriodicalId":377019,"journal":{"name":"2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124552886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：