Pub Date : 2021-01-01DOI: 10.1109/LGRS.2020.2967387
Jiajia Cai, Hao Zhou, Weimin Huang, B. Wen
Ship detection at the sea surface is important for improving human marine activities. Most existing ship detection methods for high-frequency surface wave radar (HFSWR) are based on peak and constant false alarm rate (CFAR) detection and require a coherent integration time (CIT) of several minutes. However, in such a long period, the target may not be stationary. To account for the nonstationary property, a time-frequency analysis (TFA)-based ship detection and direction finding (DF) method is proposed for HFSWR. Target ridges on the TF representation (TFR) of the echo data are detected first. Next, array snapshots are formed by sampling the extracted ridges and are used to estimate the direction of arrival (DOA). The processing results of the radar data collected at Dongshan, Fujian Province, China, show that the proposed method outperforms the CFAR method with both increased detection rates and decreased DF errors, especially under relatively low signal-to-noise ratio (SNR) scenarios.
{"title":"Ship Detection and Direction Finding Based on Time-Frequency Analysis for Compact HF Radar","authors":"Jiajia Cai, Hao Zhou, Weimin Huang, B. Wen","doi":"10.1109/LGRS.2020.2967387","DOIUrl":"https://doi.org/10.1109/LGRS.2020.2967387","url":null,"abstract":"Ship detection at the sea surface is important for improving human marine activities. Most existing ship detection methods for high-frequency surface wave radar (HFSWR) are based on peak and constant false alarm rate (CFAR) detection and require a coherent integration time (CIT) of several minutes. However, in such a long period, the target may not be stationary. To account for the nonstationary property, a time-frequency analysis (TFA)-based ship detection and direction finding (DF) method is proposed for HFSWR. Target ridges on the TF representation (TFR) of the echo data are detected first. Next, array snapshots are formed by sampling the extracted ridges and are used to estimate the direction of arrival (DOA). The processing results of the radar data collected at Dongshan, Fujian Province, China, show that the proposed method outperforms the CFAR method with both increased detection rates and decreased DF errors, especially under relatively low signal-to-noise ratio (SNR) scenarios.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"72-76"},"PeriodicalIF":4.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2020.2967387","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62473135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/LGRS.2019.2962723
Zhi He, Q. Shi, Kai Liu, Jingjing Cao, Wen Zhan, B. Cao
Mangrove species classification is of particular importance for coastal conservation and restoration. However, it is challenging to distinguish species-level differences with limited training data. In this letter, we propose an object-oriented classification method for mangrove forests by using the hyperspectral image (HSI) and the 3-D Siamese residual network. First, superpixel segmentation is utilized to obtain objects with various shapes and scales. Second, 3-D patches of each object are extracted from the original HSI, and those patches containing training samples are adopted to pairwise train the network. The 3-D spatial pyramid pooling (3-D-SPP) is added in the network to extract features in multiple scales. Finally, the abstract features of test samples are learned by the trained network, and the labels are determined by the nearest neighbor classifier within the metric space. Experiments on real mangrove hyperspectral data demonstrate the effectiveness of the proposed method in species classification of mangroves.
{"title":"Object-Oriented Mangrove Species Classification Using Hyperspectral Data and 3-D Siamese Residual Network","authors":"Zhi He, Q. Shi, Kai Liu, Jingjing Cao, Wen Zhan, B. Cao","doi":"10.1109/LGRS.2019.2962723","DOIUrl":"https://doi.org/10.1109/LGRS.2019.2962723","url":null,"abstract":"Mangrove species classification is of particular importance for coastal conservation and restoration. However, it is challenging to distinguish species-level differences with limited training data. In this letter, we propose an object-oriented classification method for mangrove forests by using the hyperspectral image (HSI) and the 3-D Siamese residual network. First, superpixel segmentation is utilized to obtain objects with various shapes and scales. Second, 3-D patches of each object are extracted from the original HSI, and those patches containing training samples are adopted to pairwise train the network. The 3-D spatial pyramid pooling (3-D-SPP) is added in the network to extract features in multiple scales. Finally, the abstract features of test samples are learned by the trained network, and the labels are determined by the nearest neighbor classifier within the metric space. Experiments on real mangrove hyperspectral data demonstrate the effectiveness of the proposed method in species classification of mangroves.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"17 1","pages":"2150-2154"},"PeriodicalIF":4.8,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2019.2962723","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45575045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/LGRS.2019.2961502
Mingming Xu, Yan Zhang, Yanguo Fan, Yanlong Chen, Dongmei Song
Endmember extraction (EE) is one important step in hyperspectral unmixing. However, some EE methods under pure-pixel assumption may work badly for highly mixed data due to the complexity of image data. In this work, we propose a linear spectral mixing model-guided artificial bee colony (LSMM-ABC) method for EE to solve the problem under a highly mixed situation. The main innovative point of this work is that each employed bee in LSMM-ABC searches food source position guided by the LSMM, rather than with a neighbor food source position. What is more, this proposed LSMM-ABC is not confined to the pure-pixel assumption. The LSMM could help employed bees to find a better solution in endmember generation based on the ABC algorithm. Experimental results on both synthetic and real Cuprite data sets show us that the proposed LSMM-ABC method can improve the overall EE accuracy compared with the EE methods for highly mixed data.
端元提取(end - member extraction, EE)是高光谱解混的重要步骤。然而，由于图像数据的复杂性，一些纯像素假设下的EE方法在高度混合的数据中可能效果不佳。本文提出了一种线性光谱混合模型引导的人工蜂群(LSMM-ABC)方法来解决高度混合情况下的EE问题。本研究的主要创新点在于LSMM- abc中的每只被雇佣的蜜蜂在LSMM的引导下搜索食物源位置，而不是与邻居的食物源位置。此外，本文提出的LSMM-ABC不局限于纯像素假设。LSMM可以帮助工蜂在基于ABC算法的端元生成中找到更好的解决方案。在合成和真实Cuprite数据集上的实验结果表明，LSMM-ABC方法与高度混合数据的EE方法相比，可以提高整体的EE精度。
{"title":"Linear Spectral Mixing Model-Guided Artificial Bee Colony Method for Endmember Generation","authors":"Mingming Xu, Yan Zhang, Yanguo Fan, Yanlong Chen, Dongmei Song","doi":"10.1109/LGRS.2019.2961502","DOIUrl":"https://doi.org/10.1109/LGRS.2019.2961502","url":null,"abstract":"Endmember extraction (EE) is one important step in hyperspectral unmixing. However, some EE methods under pure-pixel assumption may work badly for highly mixed data due to the complexity of image data. In this work, we propose a linear spectral mixing model-guided artificial bee colony (LSMM-ABC) method for EE to solve the problem under a highly mixed situation. The main innovative point of this work is that each employed bee in LSMM-ABC searches food source position guided by the LSMM, rather than with a neighbor food source position. What is more, this proposed LSMM-ABC is not confined to the pure-pixel assumption. The LSMM could help employed bees to find a better solution in endmember generation based on the ABC algorithm. Experimental results on both synthetic and real Cuprite data sets show us that the proposed LSMM-ABC method can improve the overall EE accuracy compared with the EE methods for highly mixed data.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"17 1","pages":"2145-2149"},"PeriodicalIF":4.8,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2019.2961502","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48077157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/LGRS.2019.2963106
Bangyu Wu, Delin Meng, Lingling Wang, Naihao Liu, Ying Wang
In this letter, we use a fully convolutional residual network (FCRN) for seismic impedance inversion. After training with appropriate data, the FCRN can effectively predict impedance with high accuracy, and have good robustness against noise and phase difference. However, it cannot give acceptable results in training and predicting models with different geological features. Transfer learning is later introduced to ease this problem. Marmousi2 and Overthrust models are used to verify the effectiveness of the proposed method. Tests show that after fine-tuned by five traces of Overthrust model, the FCRN trained on the Marmousi2 model can give a comparable result similarly predicted by the FCRN trained purely on the Overthrust model.
{"title":"Seismic Impedance Inversion Using Fully Convolutional Residual Network and Transfer Learning","authors":"Bangyu Wu, Delin Meng, Lingling Wang, Naihao Liu, Ying Wang","doi":"10.1109/LGRS.2019.2963106","DOIUrl":"https://doi.org/10.1109/LGRS.2019.2963106","url":null,"abstract":"In this letter, we use a fully convolutional residual network (FCRN) for seismic impedance inversion. After training with appropriate data, the FCRN can effectively predict impedance with high accuracy, and have good robustness against noise and phase difference. However, it cannot give acceptable results in training and predicting models with different geological features. Transfer learning is later introduced to ease this problem. Marmousi2 and Overthrust models are used to verify the effectiveness of the proposed method. Tests show that after fine-tuned by five traces of Overthrust model, the FCRN trained on the Marmousi2 model can give a comparable result similarly predicted by the FCRN trained purely on the Overthrust model.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"17 1","pages":"2140-2144"},"PeriodicalIF":4.8,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2019.2963106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46768455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-09DOI: 10.1109/lgrs.2020.3034677
Xu Chen, H. Sui, Jian Fang, Mingting Zhou, Chen Wu
Satellite videos have recently served as a new data source for a wide range of applications in traffic management and military surveillance. Due to its wider coverage, satellite videos show more advantages in large-scale monitoring than ground surveillance videos. However, pseudomotion background and low-resolution targets pose new challenges to moving vehicle detection in satellite videos, resulting in poor performance of conventional target detection methods when applied to satellite videos. To overcome this difficulty, we propose a novel moving vehicle detection approach using adaptive motion separation and difference accumulated trajectory. Specifically, a new indicator is designed to assist adaptive separation of moving targets and background, considering the scale invariance of vehicles in satellite videos. Meanwhile, we offer a vehicle discrimination algorithm based on a differential accumulated trajectory to distinguish the moving vehicles from the pseudomotion background. Experimental results on two satellite video data sets demonstrate that the proposed approach achieves better detection performance over the state-of-the-art moving vehicle detection methods.
{"title":"A Novel AMS-DAT Algorithm for Moving Vehicle Detection in a Satellite Video","authors":"Xu Chen, H. Sui, Jian Fang, Mingting Zhou, Chen Wu","doi":"10.1109/lgrs.2020.3034677","DOIUrl":"https://doi.org/10.1109/lgrs.2020.3034677","url":null,"abstract":"Satellite videos have recently served as a new data source for a wide range of applications in traffic management and military surveillance. Due to its wider coverage, satellite videos show more advantages in large-scale monitoring than ground surveillance videos. However, pseudomotion background and low-resolution targets pose new challenges to moving vehicle detection in satellite videos, resulting in poor performance of conventional target detection methods when applied to satellite videos. To overcome this difficulty, we propose a novel moving vehicle detection approach using adaptive motion separation and difference accumulated trajectory. Specifically, a new indicator is designed to assist adaptive separation of moving targets and background, considering the scale invariance of vehicles in satellite videos. Meanwhile, we offer a vehicle discrimination algorithm based on a differential accumulated trajectory to distinguish the moving vehicles from the pseudomotion background. Experimental results on two satellite video data sets demonstrate that the proposed approach achieves better detection performance over the state-of-the-art moving vehicle detection methods.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"36 1","pages":"1-5"},"PeriodicalIF":4.8,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3034677","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62474385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-22DOI: 10.1109/lgrs.2020.3009304
Fernando Darío Almeida García, H. Mora, G. Fraidenraich, J. Filho
Modern radar systems use square-law detectors to search and track fluctuating targets embedded in Weibull-distributed ground clutter. However, the theoretical performance analysis of square-law detectors in the presence of Weibull clutter leads to cumbersome mathematical formulations. Some studies have circumvented this problem by using approximations or mathematical artifacts to simplify calculations. In this work, we derive a closed-form and exact expression for the probability of detection (PD) of a square-law detector in the presence of exponential targets and Weibull-distributed ground clutter, given in terms of the Fox H-function. Unlike previous studies, no approximations nor simplifying assumptions are made throughout our analysis. Furthermore, we derive a fast convergent series for the referred PD by exploiting the orthogonal selection of poles in Cauchy’s residue theorem. In passing, we also obtain closed-form solutions and series representations for the probability density function and the cumulative distribution function of the sum statistics that govern the output of a square-law detector. Numerical results and Monte Carlo simulations corroborate the validity of our expressions.
{"title":"Square-Law Detection of Exponential Targets in Weibull-Distributed Ground Clutter","authors":"Fernando Darío Almeida García, H. Mora, G. Fraidenraich, J. Filho","doi":"10.1109/lgrs.2020.3009304","DOIUrl":"https://doi.org/10.1109/lgrs.2020.3009304","url":null,"abstract":"Modern radar systems use square-law detectors to search and track fluctuating targets embedded in Weibull-distributed ground clutter. However, the theoretical performance analysis of square-law detectors in the presence of Weibull clutter leads to cumbersome mathematical formulations. Some studies have circumvented this problem by using approximations or mathematical artifacts to simplify calculations. In this work, we derive a closed-form and exact expression for the probability of detection (PD) of a square-law detector in the presence of exponential targets and Weibull-distributed ground clutter, given in terms of the Fox H-function. Unlike previous studies, no approximations nor simplifying assumptions are made throughout our analysis. Furthermore, we derive a fast convergent series for the referred PD by exploiting the orthogonal selection of poles in Cauchy’s residue theorem. In passing, we also obtain closed-form solutions and series representations for the probability density function and the cumulative distribution function of the sum statistics that govern the output of a square-law detector. Numerical results and Monte Carlo simulations corroborate the validity of our expressions.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1956-1960"},"PeriodicalIF":4.8,"publicationDate":"2020-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3009304","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46449550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/LGRS.2020.3011549
C. Muehlmann, K. Nordhausen, Mengxi Yi
Multivariate measurements taken at irregularly sampled locations are a common form of data, for example, in geochemical analysis of soil. In practical considerations, predictions of these measurements at unobserved locations are of great interest. For standard multivariate spatial prediction methods it is mandatory to not only model spatial dependencies but also cross-dependencies which makes it a demanding task. Recently, a blind source separation (BSS) approach for spatial data was suggested. When using this spatial BSS (SBSS) method before the actual spatial prediction, modeling of spatial cross-dependencies is avoided, which in turn simplifies the spatial prediction task significantly. In this letter, we investigate the use of SBSS as a preprocessing tool for spatial prediction and compare it with predictions from Cokriging and neural networks in an extensive simulation study as well as a geochemical data set.
{"title":"On Cokriging, Neural Networks, and Spatial Blind Source Separation for Multivariate Spatial Prediction","authors":"C. Muehlmann, K. Nordhausen, Mengxi Yi","doi":"10.1109/LGRS.2020.3011549","DOIUrl":"https://doi.org/10.1109/LGRS.2020.3011549","url":null,"abstract":"Multivariate measurements taken at irregularly sampled locations are a common form of data, for example, in geochemical analysis of soil. In practical considerations, predictions of these measurements at unobserved locations are of great interest. For standard multivariate spatial prediction methods it is mandatory to not only model spatial dependencies but also cross-dependencies which makes it a demanding task. Recently, a blind source separation (BSS) approach for spatial data was suggested. When using this spatial BSS (SBSS) method before the actual spatial prediction, modeling of spatial cross-dependencies is avoided, which in turn simplifies the spatial prediction task significantly. In this letter, we investigate the use of SBSS as a preprocessing tool for spatial prediction and compare it with predictions from Cokriging and neural networks in an extensive simulation study as well as a geochemical data set.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1931-1935"},"PeriodicalIF":4.8,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2020.3011549","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47829030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-30DOI: 10.1109/LGRS.2019.2963065
Zenghui Zhang, Weiwei Guo, Mingjie Li, Wenxian Yu
Automatic building extraction from aerial or satellite images is a dense pixel prediction task for many applications. It demands a large number of clean label data to train a deep neural network for building extraction. But it is labor expensive to collect such pixel-wise annotated data manually. Fortunately, the building footprint data of geographic information system (GIS) maps provide a cheap way of generating building label data, but these labels are imperfect due to misalignment between the GIS maps and images. In this letter, we consider the task of learning a deep neural network to label images pixel-wise from such noisy label data for building extraction. To this end, we propose a general label noise-adaptive (NA) neural network framework consisting of a base network followed by an additional probability transition modular (PTM) which is introduced to capture the relationship between the true label and the noisy label. The parameters of the PTM can be estimated as part of the training process of the whole network by the off-the-shelf backpropagation algorithm. We conduct experiments on real-world data set to demonstrate that our proposed PTM can better handle noisy labels and improve the performance of convolutional neural networks (CNNs) trained on the noisy label data generated by GIS maps for building extraction. The experimental results indicate that being armed with our proposed PTM for fully CNN, it provides a promising solution to reduce manual annotation effort for the labor-expensive object extraction tasks from remote sensing images.
{"title":"GIS-Supervised Building Extraction With Label Noise-Adaptive Fully Convolutional Neural Network","authors":"Zenghui Zhang, Weiwei Guo, Mingjie Li, Wenxian Yu","doi":"10.1109/LGRS.2019.2963065","DOIUrl":"https://doi.org/10.1109/LGRS.2019.2963065","url":null,"abstract":"Automatic building extraction from aerial or satellite images is a dense pixel prediction task for many applications. It demands a large number of clean label data to train a deep neural network for building extraction. But it is labor expensive to collect such pixel-wise annotated data manually. Fortunately, the building footprint data of geographic information system (GIS) maps provide a cheap way of generating building label data, but these labels are imperfect due to misalignment between the GIS maps and images. In this letter, we consider the task of learning a deep neural network to label images pixel-wise from such noisy label data for building extraction. To this end, we propose a general label noise-adaptive (NA) neural network framework consisting of a base network followed by an additional probability transition modular (PTM) which is introduced to capture the relationship between the true label and the noisy label. The parameters of the PTM can be estimated as part of the training process of the whole network by the off-the-shelf backpropagation algorithm. We conduct experiments on real-world data set to demonstrate that our proposed PTM can better handle noisy labels and improve the performance of convolutional neural networks (CNNs) trained on the noisy label data generated by GIS maps for building extraction. The experimental results indicate that being armed with our proposed PTM for fully CNN, it provides a promising solution to reduce manual annotation effort for the labor-expensive object extraction tasks from remote sensing images.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"17 1","pages":"2135-2139"},"PeriodicalIF":4.8,"publicationDate":"2020-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2019.2963065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46085301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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