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IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium最新文献

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Synergetic Use of Morphological and Radar Parameter for Lunar Water Ice Detection 形态和雷达参数在月球水冰探测中的协同应用
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324319
Urvi Shroff, Bindi Dave, S. Mohan
{"title":"Synergetic Use of Morphological and Radar Parameter for Lunar Water Ice Detection","authors":"Urvi Shroff, Bindi Dave, S. Mohan","doi":"10.1109/IGARSS39084.2020.9324319","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324319","url":null,"abstract":"Radar measured elevated circular polarization ratio (CPR) from lunar polar region are not unique signature of lunar water ice deposits, It may also be caused by wavelength scale roughness e.g. fragments from fresh ejecta which forms corner reflectors. Thus, there is a need to assess the role of other remotely sensed parameters along with elevated CPR for water ice detection. The study derives the morphological parameters like crater diameter (D), crater depth (d), crater floor diameter, d/D ratio along with annual temperature, surface roughness and backscattering coefficient (S1). Association of these parameters for fresh craters and craters with water ice was studied. The results indicate that morphological parameters such as surface slope, surface roughness and d/D ratio influences crater which exhibits high CPR only in their interiors (anomalous). Furthermore, analyzing d/D ratio and surface roughness with CPR shows unambiguous separation between them. These observations emphasizes role of morphometry in detecting craters havinz water ice.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121898720","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}
引用次数: 1
Meta Network for Radar HRRP Noncooperative Target Recognition with Missing Aspects 缺失面雷达HRRP非合作目标识别的元网络
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323129
Long Tian, Bo Chen, Yang Peng, Chuan Du, Zhenhua Wu, Hongwei Liu
{"title":"Meta Network for Radar HRRP Noncooperative Target Recognition with Missing Aspects","authors":"Long Tian, Bo Chen, Yang Peng, Chuan Du, Zhenhua Wu, Hongwei Liu","doi":"10.1109/IGARSS39084.2020.9323129","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323129","url":null,"abstract":"We propose a meta network (MNet) for the problem of target-aspect missing in radar high-resolution range profile (HRRP)-based noncooperative target recognition, where a classifier must be generalized to new aspects not seen in the training set, given only a small number of HRRP data of each new aspect. The MNet is a time domain convolutional neural network (TCNN) that is built based upon recent progress in meta-learning. In effect, it learns a model that is easy and fast to fine-tune, allowing the adaptation to happen in the right space for fast learning. Besides, we construct a new controllable HRRP dataset suitable for the scenario of noncooperative target-aspect missing using electromagnetic simulation. Compared with the traditional methods, the MNet is more efficient and could achieve better performance. Extensive experiments on the simulated HRRP dataset are conducted to illustrate the effectiveness of the proposed method.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116820413","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}
引用次数: 4
Experimental Study on Along Track Target Velocity Estimation for Multiple Aperture SAR-MTI Configuration 多孔径SAR-MTI结构沿航迹目标速度估计的实验研究
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324313
K. Suwa, T. Wakayama
{"title":"Experimental Study on Along Track Target Velocity Estimation for Multiple Aperture SAR-MTI Configuration","authors":"K. Suwa, T. Wakayama","doi":"10.1109/IGARSS39084.2020.9324313","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324313","url":null,"abstract":"SAR (Synthetic Aperture Radar) moving target along-track velocity estimation method for multichannel system is proposed. The proposed method works on the co-registered multichannel SAR images and employ sub-aperture processing to enhance the robustness to the clutter and noise. The velocity estimation performance of the proposed algorithm is shown through airborne Ku-band three channel SAR experiments, and it has been shown that the accuracy of the along track velocity estimation of moving target at the range of about 2.5km is on the order of 0.2 m/s.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117033784","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}
引用次数: 1
Spatial-Spectral Autoencoder Networks for Hyperspectral Unmixing 用于高光谱解混的空间光谱自编码器网络
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324696
Yongfa Huang, Jie Li, Lin Qi, Ying Wang, Xinbo Gao
{"title":"Spatial-Spectral Autoencoder Networks for Hyperspectral Unmixing","authors":"Yongfa Huang, Jie Li, Lin Qi, Ying Wang, Xinbo Gao","doi":"10.1109/IGARSS39084.2020.9324696","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324696","url":null,"abstract":"We present a spatial-spectral autoencoder (SSAE) for hyperspectral unmixing, including a net for endmember extraction (EENet) and a net for abundance estimation (AENet). The EENet exploits the spatial information in hyperspectral image by a “many to one” strategy, i.e., the abundance of a pixel is combined by the abundances of its adjacent pixels. The idea is based on the assumption: once an endmember is mixed in a pixel, it is mixed in the surrounding pixels with high probability. The strategy promotes a continuous and smooth spatial distribution of abundances, and it is more effective than the other methods for endmember extraction. Besides, to make full use of the rich spectral information and obtain more accurate abundances, we design an AENet, which applies the deep convolutional neural network to estimate the abundances with the endmembers acquired from the EENet. The experiments are conducted on two real datasets, which show the SSAE outperforms the state-of-the-art methods.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129469255","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}
引用次数: 8
Multi-Agents Path Planning for a Swarm of Unmanned Aerial Vehicles 无人机群的多智能体路径规划
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324503
Richard Carney, M. Chyba, Chris Gray, A. Trimble
{"title":"Multi-Agents Path Planning for a Swarm of Unmanned Aerial Vehicles","authors":"Richard Carney, M. Chyba, Chris Gray, A. Trimble","doi":"10.1109/IGARSS39084.2020.9324503","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324503","url":null,"abstract":"The objective of swarms is to enable multiple agents to collaborate toward a common goal, as one would find in a remote sensing setting. In this paper we focus on swarms of unmanned aerial vehicles (UAVs), which for instance have an objective to optimize the survey of a prescribed area and/or the detection of a specific object. Instructing each individual agent from a central command control quickly becomes inefficient, even for small groups of agents. Agreement protocol is done locally by the multi-agents without external user input. Because of the wide variety of conditions UAVs can face, the algorithm needs to be robust despite external disturbances.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129662052","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}
引用次数: 2
An Infinity-Norm-Based Phase Unwrapping Method with TSPA Framework for Multi-Baseline SAR Interferograms 基于TSPA框架的多基线SAR干涉图无限范数相位展开方法
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323551
Yang Lan, Hanwen Yu, M. Xing, Jixiang Fu
{"title":"An Infinity-Norm-Based Phase Unwrapping Method with TSPA Framework for Multi-Baseline SAR Interferograms","authors":"Yang Lan, Hanwen Yu, M. Xing, Jixiang Fu","doi":"10.1109/IGARSS39084.2020.9323551","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323551","url":null,"abstract":"Phase unwrapping (PU) is a key step for the synthetic aperture radar (SAR) interferometry (InSAR). Single-baseline (SB) PU and multi-baseline (MB) PU are two independently developed technologies, each of which has its own advantages and disadvantages. A two-stage programming-based MB PU method (TSPA) proposed by Yu [1] establishes a connection between the MB and SB PU methods. TSPA breaks the limitation of the phase continuity assumption by using the Chinese remainder theorem (CRT), and uses the minimum-cost flow (MCF) optimization model to obtain the PU result. TSPA can be regarded as a framework for solving MB PU problems. In this paper, we studied how to transplant the infinity-norm ($L^{infty}$-norm) optimization model into TSPA framework. Under the TSPA MB PU framework, a $L^{infty}$-norm based MB PU method (referred to as Inf-TSPA) is proposed to solve the problem of low PU accuracy of the $L^{infty}$-norm SB PU method. The experimental results on the simulated and the realistic MB InSAR data sets verify that the performance of Inf-TSPA is significantly improved compared to the $L^{infty}$-norm SB PU method.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129758643","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}
引用次数: 0
Linear Array 3-D SAR Sparse Imaging via Convolutional Neural Network 基于卷积神经网络的线性阵列三维SAR稀疏成像
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324030
Mou Wang, Shunjun Wei, Jun Shi, Yue Wu, Jiadian Liang, Qizhe Qu
{"title":"Linear Array 3-D SAR Sparse Imaging via Convolutional Neural Network","authors":"Mou Wang, Shunjun Wei, Jun Shi, Yue Wu, Jiadian Liang, Qizhe Qu","doi":"10.1109/IGARSS39084.2020.9324030","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324030","url":null,"abstract":"Compressed sensing theory has attracted extensive attention in the field of linear array 3-D Synthetic Aperture Radar (SAR) sparse imaging. However, conventional CS-based algorithms always suffer from quite huge computational cost. In this paper, we propose a new method for 3-D SAR sparse imaging based on convolutional neural network (CNN). Inspired by the work of ISTA-NET, a complex-valued version for imaging tasks is modified. Furthermore, we introduce a approximate phase correction scheme for 3-D imaging, it makes the proposed method works with only a constant measurement matrix corresponding to any slice. Moreover, Using a random training strategy, ISTA-NET networks for 3-D SAR imaging are effectively trained. Experimental results demonstrate that the proposed method outperforms conventional ISTA large margins in both accuracy and speed.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129952435","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}
引用次数: 1
NOAA20 and S-NPP VIIRS Land Surface Temperature Product Validation and Inter-Comparison NOAA20和S-NPP VIIRS地表温度产品的验证与比对
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323853
Yuling Liu, Yunyue Yu, P. Yu, Heshun Wang
{"title":"NOAA20 and S-NPP VIIRS Land Surface Temperature Product Validation and Inter-Comparison","authors":"Yuling Liu, Yunyue Yu, P. Yu, Heshun Wang","doi":"10.1109/IGARSS39084.2020.9323853","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323853","url":null,"abstract":"The enterprise Land Surface Temperature (LST) algorithm has been operationally implemented for Visible Infrared Imager Radiometer Suite (VIIRS) onboard both NOAA 20 (N20) and Suomi National Polar-orbiting Partnership (S-NPP) satellite since September, 2019. This study presents the validation of the two LST products. The ground based measurements from Baseline Surface Radiation Network (BSRN) and Surface Radiation Budget Network (SURFRAD) were used to estimate the quantitative uncertainty of the LST product. The validation results present a similarly close agreement between ground observations and satellite estimations from both N20 and S-NPP VIIRS LST products. The accuracy is about -0.4 K for N20 and -0.3 K for S-NPP and the precision is about 1.9 K for both LST products over SURFRAD sites. Similar performance is achieved over BSRN sites. In addition, the global inter-comparison of the two LST products were presented and analyzed.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128497668","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}
引用次数: 0
Airport Detection Based on Saliency Analysis and Geometric Feature Detection for Remote Sensing Images 基于显著性分析和几何特征检测的遥感影像机场检测
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323253
Wanning Zhu, Qijian Zhang, Li-bao Zhang
{"title":"Airport Detection Based on Saliency Analysis and Geometric Feature Detection for Remote Sensing Images","authors":"Wanning Zhu, Qijian Zhang, Li-bao Zhang","doi":"10.1109/IGARSS39084.2020.9323253","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323253","url":null,"abstract":"Owing to the complicated background information and large data volume in remote sensing (RS) images, it's difficult to detect airport precisely and efficiently. In this paper, we propose a credible airport detection method based on saliency analysis and geometric feature detection. On the one hand, we use a novel saliency analysis model to measure both global contrast and spatial unity in RS images, by which the most salient region can be extracted accurately and the background can be suppressed preferably. On the other hand, considering the geometric features of the airport, a feature descriptor is conducted to detect proper hole structures and line segments in the saliency map. The experimental results indicate that our proposal outperforms existing saliency analysis models and shows good performance in the detection of the airport.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128724549","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}
引用次数: 0
A Novel General Semisupervised Deep Learning Framework for Classification and Regression with Remote Sensing Images 一种新的用于遥感图像分类与回归的通用半监督深度学习框架
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323932
Zhao Chen, Guangchen Chen, F. Zhou, Bin Yang, Lili Wang, Qiong Liu, Yonghang Chen
{"title":"A Novel General Semisupervised Deep Learning Framework for Classification and Regression with Remote Sensing Images","authors":"Zhao Chen, Guangchen Chen, F. Zhou, Bin Yang, Lili Wang, Qiong Liu, Yonghang Chen","doi":"10.1109/IGARSS39084.2020.9323932","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323932","url":null,"abstract":"Remote sensing image analysis often involves image-level classification and/ or regression. One major problem with remote sensing data is that it is difficult to obtain abundant precise manual annotations to train fully supervised deep networks that have achieved great success in computer vision. Therefore, this paper proposes a novel general semisupervised framework (GSF) which only requires a small amount of annotated samples for training. It employs a new hybrid (dis) similarity to characterize different aspects of the images and realizes label propagation while fine- tuning a deep neural network (NN). As shown by the experimental results, GSF outperforms several supervised baselines and state-of-the-art semisupervised models in classification and regression.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128217400","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}
引用次数: 1
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