Pub Date : 2017-10-01DOI: 10.1109/LGRS.2017.2726898
D. Warde, S. Torres
The matched-autocorrelation spectrum-width estimator is introduced; statistics are derived and compared to those of the conventional estimator. It is demonstrated that the proposed estimator exhibits improved performance for narrow spectrum widths without increased computational complexity.
{"title":"Spectrum Width Estimation Using Matched Autocorrelations","authors":"D. Warde, S. Torres","doi":"10.1109/LGRS.2017.2726898","DOIUrl":"https://doi.org/10.1109/LGRS.2017.2726898","url":null,"abstract":"The matched-autocorrelation spectrum-width estimator is introduced; statistics are derived and compared to those of the conventional estimator. It is demonstrated that the proposed estimator exhibits improved performance for narrow spectrum widths without increased computational complexity.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"14 1","pages":"1661-1664"},"PeriodicalIF":4.8,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2017.2726898","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46536858","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 : 2017-09-03DOI: 10.3997/2214-4609.201702040
Lichao Liu, D. Grombacher, E. Auken, J. Larsen
Powerline harmonics are often the primary noise source in surface nuclear magnetic resonance (NMR) measurements. State-of-the-art techniques, such as notch filtering, Wiener filtering, and model-based subtraction, have been demonstrated to greatly mitigate powerline harmonic noise, but these approaches break down when one of the powerline harmonics has a frequency close to or coincident with the Larmor frequency $f_{L}$ , referred to as a co-frequency harmonic. We propose a hybrid scheme where model-based subtraction of powerline harmonics is coupled with data from a synchronous reference coil to specifically subtract the co-frequency harmonic component. In standard model-based subtraction of powerline harmonics, a sinusoidal model of all harmonic components is fit to the data and subtracted. In the new approach, the amplitude and phase of the co-frequency harmonic are determined by a sinusoidal model fit to the synchronous noise-only data recorded in a reference coil. From the reference coil co-frequency model, the co-frequency harmonic in the primary coil is estimated using relationships between the amplitude and phase of the co-frequency harmonic in the two coils established during noise-only segments. By utilizing data from the reference coil to model the co-frequency harmonic, accidental fitting of the surface NMR signal is avoided. We investigate the efficiency of the method using a synthetic surface NMR signal embedded in noise-only data recorded in Denmark. Our results demonstrate that the co-frequency powerline harmonic can be removed efficiently without distorting the surface NMR signal and the new method performs better than standard methods.
{"title":"Removal of Co-Frequency Powerline Harmonics From Multichannel Surface NMR Data","authors":"Lichao Liu, D. Grombacher, E. Auken, J. Larsen","doi":"10.3997/2214-4609.201702040","DOIUrl":"https://doi.org/10.3997/2214-4609.201702040","url":null,"abstract":"Powerline harmonics are often the primary noise source in surface nuclear magnetic resonance (NMR) measurements. State-of-the-art techniques, such as notch filtering, Wiener filtering, and model-based subtraction, have been demonstrated to greatly mitigate powerline harmonic noise, but these approaches break down when one of the powerline harmonics has a frequency close to or coincident with the Larmor frequency $f_{L}$ , referred to as a co-frequency harmonic. We propose a hybrid scheme where model-based subtraction of powerline harmonics is coupled with data from a synchronous reference coil to specifically subtract the co-frequency harmonic component. In standard model-based subtraction of powerline harmonics, a sinusoidal model of all harmonic components is fit to the data and subtracted. In the new approach, the amplitude and phase of the co-frequency harmonic are determined by a sinusoidal model fit to the synchronous noise-only data recorded in a reference coil. From the reference coil co-frequency model, the co-frequency harmonic in the primary coil is estimated using relationships between the amplitude and phase of the co-frequency harmonic in the two coils established during noise-only segments. By utilizing data from the reference coil to model the co-frequency harmonic, accidental fitting of the surface NMR signal is avoided. We investigate the efficiency of the method using a synthetic surface NMR signal embedded in noise-only data recorded in Denmark. Our results demonstrate that the co-frequency powerline harmonic can be removed efficiently without distorting the surface NMR signal and the new method performs better than standard methods.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"15 1","pages":"53-57"},"PeriodicalIF":4.8,"publicationDate":"2017-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46781783","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 : 2017-09-01DOI: 10.1109/LGRS.2017.2720166
Hicham Ezzine, A. Bouziane, D. Ouazar, M. Hasnaoui
This letter attempts to explore the potential sensitivity of the well-known spatial downscaling technique of coarse precipitation data to some bioclimatic stages of the Mediterranean area. For this purpose, first, an open data set covering a period of 15 years, including TRMM3B43, normalized difference vegetation index (NDVI), DEM, and rain gauge station measurements, was prepared. Then the NDVI-based spatial downscaling technique was applied over Morocco without taking account of bioclimatic stages. Second, based on the same data set, the key step of the downscaling approach (regression between TRMM3B43 and NDVI) was analyzed in five bioclimatic stages in order to assess the approach’s sensitivity. This letter demonstrated that the spatial downscaling approach performs well in the subhumid, semiarid, and in the arid bioclimatic stages, to a lesser extent. However, the approach seems to be sensitive and not adapted to the Saharan and humid stages.
{"title":"Sensitivity of NDVI-Based Spatial Downscaling Technique of Coarse Precipitation to Some Mediterranean Bioclimatic Stages","authors":"Hicham Ezzine, A. Bouziane, D. Ouazar, M. Hasnaoui","doi":"10.1109/LGRS.2017.2720166","DOIUrl":"https://doi.org/10.1109/LGRS.2017.2720166","url":null,"abstract":"This letter attempts to explore the potential sensitivity of the well-known spatial downscaling technique of coarse precipitation data to some bioclimatic stages of the Mediterranean area. For this purpose, first, an open data set covering a period of 15 years, including TRMM3B43, normalized difference vegetation index (NDVI), DEM, and rain gauge station measurements, was prepared. Then the NDVI-based spatial downscaling technique was applied over Morocco without taking account of bioclimatic stages. Second, based on the same data set, the key step of the downscaling approach (regression between TRMM3B43 and NDVI) was analyzed in five bioclimatic stages in order to assess the approach’s sensitivity. This letter demonstrated that the spatial downscaling approach performs well in the subhumid, semiarid, and in the arid bioclimatic stages, to a lesser extent. However, the approach seems to be sensitive and not adapted to the Saharan and humid stages.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"14 1","pages":"1518-1521"},"PeriodicalIF":4.8,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2017.2720166","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42496933","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 : 2017-08-29DOI: 10.1109/LGRS.2017.2729159
Ning Wang, Yinghua Wang, Hongwei Liu, Qunsheng Zuo, Jinglu He
Target discrimination has been one of the hottest issues in the interpretation of synthetic aperture radar (SAR) images. However, the presence of speckle noise and the absence of robust features make SAR discrimination difficult to deal with. Recently, convolutional neural network has obtained state-of-the-art results in pattern recognition. In this letter, we propose a target discrimination framework that jointly uses intensity and edge information of SAR images. This framework contains three parts, namely, feature extraction block, feature fusion block, and final classification block. In addition, a novel feature fusion method that can preserve the spatial relationship of different features is introduced. Experimental results on the miniSAR data demonstrate the effectiveness of our method.
{"title":"Feature-Fused SAR Target Discrimination Using Multiple Convolutional Neural Networks","authors":"Ning Wang, Yinghua Wang, Hongwei Liu, Qunsheng Zuo, Jinglu He","doi":"10.1109/LGRS.2017.2729159","DOIUrl":"https://doi.org/10.1109/LGRS.2017.2729159","url":null,"abstract":"Target discrimination has been one of the hottest issues in the interpretation of synthetic aperture radar (SAR) images. However, the presence of speckle noise and the absence of robust features make SAR discrimination difficult to deal with. Recently, convolutional neural network has obtained state-of-the-art results in pattern recognition. In this letter, we propose a target discrimination framework that jointly uses intensity and edge information of SAR images. This framework contains three parts, namely, feature extraction block, feature fusion block, and final classification block. In addition, a novel feature fusion method that can preserve the spatial relationship of different features is introduced. Experimental results on the miniSAR data demonstrate the effectiveness of our method.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"14 1","pages":"1695-1699"},"PeriodicalIF":4.8,"publicationDate":"2017-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2017.2729159","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46970045","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 : 2017-08-17DOI: 10.1109/LGRS.2017.2733719
C. Hu, R. Wang, C. Liu, T. Zhang, W. Li
A novel insect orientation extraction method is proposed based on the target polarization scattering matrix (PSM) estimation, which is applicable for traditional vertical-looking insect radar with noncoherent reception as well as the coherent radar. The insect echo signal at different polarization directions on the radar polarization plane is usually acquired by means of rotating linearly polarized antenna. In this letter, the insect echo signal is first used to accurately estimate insect PSM by an iterative algorithm based on the second-order polynomial approximation. Meanwhile, the Cramer–Rao lower bound is also analyzed to test the estimation performance. Next, based on the assumption that the target orientation is consistent with the dominant eigenvector, the insect orientation is extracted from the estimated PSM. Finally, both theoretical simulations and real experimental data are used to validate the effectiveness and feasibility of our proposed method, which can achieve good orientation estimation accuracy at low signal-to-noise ratio.
{"title":"Accurate Insect Orientation Extraction Based on Polarization Scattering Matrix Estimation","authors":"C. Hu, R. Wang, C. Liu, T. Zhang, W. Li","doi":"10.1109/LGRS.2017.2733719","DOIUrl":"https://doi.org/10.1109/LGRS.2017.2733719","url":null,"abstract":"A novel insect orientation extraction method is proposed based on the target polarization scattering matrix (PSM) estimation, which is applicable for traditional vertical-looking insect radar with noncoherent reception as well as the coherent radar. The insect echo signal at different polarization directions on the radar polarization plane is usually acquired by means of rotating linearly polarized antenna. In this letter, the insect echo signal is first used to accurately estimate insect PSM by an iterative algorithm based on the second-order polynomial approximation. Meanwhile, the Cramer–Rao lower bound is also analyzed to test the estimation performance. Next, based on the assumption that the target orientation is consistent with the dominant eigenvector, the insect orientation is extracted from the estimated PSM. Finally, both theoretical simulations and real experimental data are used to validate the effectiveness and feasibility of our proposed method, which can achieve good orientation estimation accuracy at low signal-to-noise ratio.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"14 1","pages":"1755-1759"},"PeriodicalIF":4.8,"publicationDate":"2017-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2017.2733719","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42386586","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 : 2017-08-15DOI: 10.1109/LGRS.2017.2723763
Jianzhe Lin, Chen He, Z. J. Wang, Shuying Li
Recent advances on remote sensing techniques allow easier access to imaging spectrometer data. Manually labeling and processing of such collected hyperspectral images (HSIs) with a vast quantities of samples and a large number of bands is labor and time consuming. To relieve these manual processes, machine learning based HSI processing methods have attracted increasing research attention. A major assumption in many machine learning problems is that the training and testing data are in the same feature space and follow the same distribution. However, this assumption doesn’t always hold true in many real world problems, especially in certain HSI processing problems with extremely insufficient or even without training samples. In this letter, we present a transfer learning framework to address this unsupervised challenge (i.e., without training samples in the target domain), by making the following three main contributions: 1) to the best of our knowledge, this is the first time for transfer learning framework to be used for the classification of totally unknown target HSI data with no training samples; 2) the characteristics of HSI are learned on dual spaces to exploit its structure knowledge to better label HSI samples; and 3) two specific new scenarios suitable for transfer learning are investigated. Experimental results on several real world HSIs support the superiority of the proposed work.
{"title":"Structure Preserving Transfer Learning for Unsupervised Hyperspectral Image Classification","authors":"Jianzhe Lin, Chen He, Z. J. Wang, Shuying Li","doi":"10.1109/LGRS.2017.2723763","DOIUrl":"https://doi.org/10.1109/LGRS.2017.2723763","url":null,"abstract":"Recent advances on remote sensing techniques allow easier access to imaging spectrometer data. Manually labeling and processing of such collected hyperspectral images (HSIs) with a vast quantities of samples and a large number of bands is labor and time consuming. To relieve these manual processes, machine learning based HSI processing methods have attracted increasing research attention. A major assumption in many machine learning problems is that the training and testing data are in the same feature space and follow the same distribution. However, this assumption doesn’t always hold true in many real world problems, especially in certain HSI processing problems with extremely insufficient or even without training samples. In this letter, we present a transfer learning framework to address this unsupervised challenge (i.e., without training samples in the target domain), by making the following three main contributions: 1) to the best of our knowledge, this is the first time for transfer learning framework to be used for the classification of totally unknown target HSI data with no training samples; 2) the characteristics of HSI are learned on dual spaces to exploit its structure knowledge to better label HSI samples; and 3) two specific new scenarios suitable for transfer learning are investigated. Experimental results on several real world HSIs support the superiority of the proposed work.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"14 1","pages":"1656-1660"},"PeriodicalIF":4.8,"publicationDate":"2017-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2017.2723763","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43733146","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 : 2017-08-11DOI: 10.1109/LGRS.2017.2729512
Xiaoyang Wang, Zhenming Peng, Ping Zhang, Yanmin He
Infrared small target detection is one of the key techniques in the infrared search and track system. Frequency differences among target, background, and noise are often important information for target detection. In this letter, a nonnegativity-constrained variational mode decomposition (NVMD) method is proposed. Unlike the traditional frequency-domain methods, the proposed method can adaptively decompose the input signal into several separated band-limited subsignals, with the nonnegativity constraint. First, a bandpass filter is used as a preprocessing step. Second, by exploring the frequency and nonnegativity properties of the small target, the NVMD model is constructed. The potential target subsignal can be obtained by solving the NVMD model. By performing threshold segmentation on the potential target subsignal, we can obtain the detection result of the infrared small target. Experiments on six real infrared image sequences demonstrate that the proposed method has a good performance in target enhancement and background suppression. Additionally, the proposed method shows strong robustness under various backgrounds.
{"title":"Infrared Small Target Detection via Nonnegativity-Constrained Variational Mode Decomposition","authors":"Xiaoyang Wang, Zhenming Peng, Ping Zhang, Yanmin He","doi":"10.1109/LGRS.2017.2729512","DOIUrl":"https://doi.org/10.1109/LGRS.2017.2729512","url":null,"abstract":"Infrared small target detection is one of the key techniques in the infrared search and track system. Frequency differences among target, background, and noise are often important information for target detection. In this letter, a nonnegativity-constrained variational mode decomposition (NVMD) method is proposed. Unlike the traditional frequency-domain methods, the proposed method can adaptively decompose the input signal into several separated band-limited subsignals, with the nonnegativity constraint. First, a bandpass filter is used as a preprocessing step. Second, by exploring the frequency and nonnegativity properties of the small target, the NVMD model is constructed. The potential target subsignal can be obtained by solving the NVMD model. By performing threshold segmentation on the potential target subsignal, we can obtain the detection result of the infrared small target. Experiments on six real infrared image sequences demonstrate that the proposed method has a good performance in target enhancement and background suppression. Additionally, the proposed method shows strong robustness under various backgrounds.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"14 1","pages":"1700-1704"},"PeriodicalIF":4.8,"publicationDate":"2017-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2017.2729512","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45405779","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 : 2017-08-11DOI: 10.1109/LGRS.2017.2731997
Gong Cheng, Zhenpeng Li, Xiwen Yao, Lei Guo, Zhongliang Wei
More recently, remote sensing image classification has been moving from pixel-level interpretation to scene-level semantic understanding, which aims to label each scene image with a specific semantic class. While significant efforts have been made in developing various methods for remote sensing image scene classification, most of them rely on handcrafted features. In this letter, we propose a novel feature representation method for scene classification, named bag of convolutional features (BoCF). Different from the traditional bag of visual words-based methods in which the visual words are usually obtained by using handcrafted feature descriptors, the proposed BoCF generates visual words from deep convolutional features using off-the-shelf convolutional neural networks. Extensive evaluations on a publicly available remote sensing image scene classification benchmark and comparison with the state-of-the-art methods demonstrate the effectiveness of the proposed BoCF method for remote sensing image scene classification.
{"title":"Remote Sensing Image Scene Classification Using Bag of Convolutional Features","authors":"Gong Cheng, Zhenpeng Li, Xiwen Yao, Lei Guo, Zhongliang Wei","doi":"10.1109/LGRS.2017.2731997","DOIUrl":"https://doi.org/10.1109/LGRS.2017.2731997","url":null,"abstract":"More recently, remote sensing image classification has been moving from pixel-level interpretation to scene-level semantic understanding, which aims to label each scene image with a specific semantic class. While significant efforts have been made in developing various methods for remote sensing image scene classification, most of them rely on handcrafted features. In this letter, we propose a novel feature representation method for scene classification, named bag of convolutional features (BoCF). Different from the traditional bag of visual words-based methods in which the visual words are usually obtained by using handcrafted feature descriptors, the proposed BoCF generates visual words from deep convolutional features using off-the-shelf convolutional neural networks. Extensive evaluations on a publicly available remote sensing image scene classification benchmark and comparison with the state-of-the-art methods demonstrate the effectiveness of the proposed BoCF method for remote sensing image scene classification.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"14 1","pages":"1735-1739"},"PeriodicalIF":4.8,"publicationDate":"2017-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2017.2731997","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44320984","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 : 2017-08-11DOI: 10.1109/LGRS.2017.2733558
Ke Wu, Yanfei Zhong, Xianmin Wang, Weiwei Sun
Extracting subpixel land-cover change detection (SLCCD) information is important when multitemporal remotely sensed images with different resolutions are available. The general steps are as follows. First, soft classification is applied to a low-resolution (LR) image to generate the proportion of each class. Second, the proportion differences are produced by the use of another high-resolution (HR) image and used as the input of subpixel mapping. Finally, a subpixel sharpened difference map can be generated. However, the prior HR land-cover map is only used to compare with the enhanced map of LR image for change detection, which leads to a nonideal SLCCD result. In this letter, we present a new approach based on a back-propagation neural network (BPNN) with a HR map (BPNN_HRM), in which a supervised model is introduced into SLCCD for the first time. The known information of the HR land-cover map is adequately employed to train the BPNN, whether it predates or postdates the LR image, so that a subpixel change detection map can be effectively generated. In order to evaluate the performance of the proposed algorithm, it was compared with four state-of-the-art methods. The experimental results confirm that the BPNN_HRM method outperforms the other traditional methods in providing a more detailed map for change detection.
{"title":"A Novel Approach to Subpixel Land-Cover Change Detection Based on a Supervised Back-Propagation Neural Network for Remotely Sensed Images With Different Resolutions","authors":"Ke Wu, Yanfei Zhong, Xianmin Wang, Weiwei Sun","doi":"10.1109/LGRS.2017.2733558","DOIUrl":"https://doi.org/10.1109/LGRS.2017.2733558","url":null,"abstract":"Extracting subpixel land-cover change detection (SLCCD) information is important when multitemporal remotely sensed images with different resolutions are available. The general steps are as follows. First, soft classification is applied to a low-resolution (LR) image to generate the proportion of each class. Second, the proportion differences are produced by the use of another high-resolution (HR) image and used as the input of subpixel mapping. Finally, a subpixel sharpened difference map can be generated. However, the prior HR land-cover map is only used to compare with the enhanced map of LR image for change detection, which leads to a nonideal SLCCD result. In this letter, we present a new approach based on a back-propagation neural network (BPNN) with a HR map (BPNN_HRM), in which a supervised model is introduced into SLCCD for the first time. The known information of the HR land-cover map is adequately employed to train the BPNN, whether it predates or postdates the LR image, so that a subpixel change detection map can be effectively generated. In order to evaluate the performance of the proposed algorithm, it was compared with four state-of-the-art methods. The experimental results confirm that the BPNN_HRM method outperforms the other traditional methods in providing a more detailed map for change detection.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"14 1","pages":"1750-1754"},"PeriodicalIF":4.8,"publicationDate":"2017-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2017.2733558","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47110021","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 : 2017-08-11DOI: 10.1109/LGRS.2017.2730849
Weilin Huang, Runqiu Wang, Yang Zhou, Xiaoqing Chen
Seismic data are highly corrupted by noise or unwanted energies arising from different kinds of sources. In general, seismic noise can be divided into two categories, namely, coherent noise and random noise, and is treated with essentially different methods. Traditional methods often utilize the differences in frequency, wavenumber, or amplitude to separate signal and noise. However, the application of traditional methods is limited if the above-mentioned differences are too small to distinguish. For this reason, we have proposed a novel morphology-based technique to simultaneously attenuate random noise and coherent noise, i.e., to extract the useful signal. In this technique, we first flatten the signal by normal move out correction or other alternative approaches. For the extraction of the flatten reflections, we propose dual-directional mathematical morphological filtering, which can detect morphological information of the seismic waveforms from two orthogonal directions and then separate signal and other unwanted energy utilizing their difference in morphological scales. Application of the proposed technique on synthetic and field data examples demonstrates a successful performance.
{"title":"Simultaneous Coherent and Random Noise Attenuation by Morphological Filtering With Dual-Directional Structuring Element","authors":"Weilin Huang, Runqiu Wang, Yang Zhou, Xiaoqing Chen","doi":"10.1109/LGRS.2017.2730849","DOIUrl":"https://doi.org/10.1109/LGRS.2017.2730849","url":null,"abstract":"Seismic data are highly corrupted by noise or unwanted energies arising from different kinds of sources. In general, seismic noise can be divided into two categories, namely, coherent noise and random noise, and is treated with essentially different methods. Traditional methods often utilize the differences in frequency, wavenumber, or amplitude to separate signal and noise. However, the application of traditional methods is limited if the above-mentioned differences are too small to distinguish. For this reason, we have proposed a novel morphology-based technique to simultaneously attenuate random noise and coherent noise, i.e., to extract the useful signal. In this technique, we first flatten the signal by normal move out correction or other alternative approaches. For the extraction of the flatten reflections, we propose dual-directional mathematical morphological filtering, which can detect morphological information of the seismic waveforms from two orthogonal directions and then separate signal and other unwanted energy utilizing their difference in morphological scales. Application of the proposed technique on synthetic and field data examples demonstrates a successful performance.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"14 1","pages":"1720-1724"},"PeriodicalIF":4.8,"publicationDate":"2017-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2017.2730849","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49191881","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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