Pub Date : 2015-07-26DOI: 10.1109/IGARSS.2015.7326028
Brian R. Johnson, A. Leon, S. Khalsa
Understanding the societal and ecological impacts of a rapidly warming Arctic, declining Arctic sea ice and the potentially strong positive climate feedback caused by thawing permafrost are key challenges in cryospheric science. Predicting how a changing climate may be driving these changes and how feedback processes in the cryosphere affect climate requires continuation of long-term satellite observations, intensive field and airborne campaigns, and new ways to analyze observational data and to integrate them with large-scale Earth system models. The National Snow and Ice Data Center, which is a primary archive for snow and ice data in the United States, is focused on making Earth observations more discoverable, accessible and providing new capabilities to visualize and synthesize diverse satellite, airborne and field data in ways that facilitate data use and accelerates scientific discovery.
{"title":"Data management in the ERA of a rapidly changing cryosphere","authors":"Brian R. Johnson, A. Leon, S. Khalsa","doi":"10.1109/IGARSS.2015.7326028","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326028","url":null,"abstract":"Understanding the societal and ecological impacts of a rapidly warming Arctic, declining Arctic sea ice and the potentially strong positive climate feedback caused by thawing permafrost are key challenges in cryospheric science. Predicting how a changing climate may be driving these changes and how feedback processes in the cryosphere affect climate requires continuation of long-term satellite observations, intensive field and airborne campaigns, and new ways to analyze observational data and to integrate them with large-scale Earth system models. The National Snow and Ice Data Center, which is a primary archive for snow and ice data in the United States, is focused on making Earth observations more discoverable, accessible and providing new capabilities to visualize and synthesize diverse satellite, airborne and field data in ways that facilitate data use and accelerates scientific discovery.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122308864","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 : 2015-07-26DOI: 10.1109/IGARSS.2015.7326890
J. Querol, Alberto Alonso Arroyo, Raul Onrubia Ibáñez, D. Pascual, Adriano Camps
Radio-Frequency Interference (RFI) is a growing problem specially for those systems that work with low power signals such as passive remote sensing instruments. Consequently, RFI mitigation techniques are currently under development. This works aims at evaluating back-end mitigation algorithms in terms of their probability of detection and mitigation performance. Results show that Wavelet Denoising (WD), and Multiresolution Fourier Transform (MFT) are the best techniques in most scenarios, specially for GNSS-based instruments.
{"title":"Assessment of back-end RFI mitigation techniques in passive remote sensing","authors":"J. Querol, Alberto Alonso Arroyo, Raul Onrubia Ibáñez, D. Pascual, Adriano Camps","doi":"10.1109/IGARSS.2015.7326890","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326890","url":null,"abstract":"Radio-Frequency Interference (RFI) is a growing problem specially for those systems that work with low power signals such as passive remote sensing instruments. Consequently, RFI mitigation techniques are currently under development. This works aims at evaluating back-end mitigation algorithms in terms of their probability of detection and mitigation performance. Results show that Wavelet Denoising (WD), and Multiresolution Fourier Transform (MFT) are the best techniques in most scenarios, specially for GNSS-based instruments.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"232 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122392977","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 : 2015-07-26DOI: 10.1109/igarss.2015.7326092
J. Toporkov, M. Sletten
Joint statistical properties of range-resolved sea backscatter at different polarizations are investigated using large Monte Carlo ensembles of numerically generated data. The simulations are based on the first-principles boundary integral equation technique in the two-dimensional (2-D) space and produce noise-free sets of backscatter corresponding to well-defined wind conditions. This study focuses on the joint probability density function (PDF) of vertically (VV) and horizontally (HH) polarized clutter intensities, investigating the behavior of the function's orientation and shape with variations in the incidence angle and wind speed. Comparisons with the results generated using coherent formulation of the popular Two-Scale Model reveal strengths and weaknesses of the latter in reproducing detailed statistical descriptors of sea clutter such as the joint VV-HH PDF.
{"title":"Investigation of joint probability density function of high-resolution VV- and HH-polarized X-band sea backscatter obtained through direct numerical simulations","authors":"J. Toporkov, M. Sletten","doi":"10.1109/igarss.2015.7326092","DOIUrl":"https://doi.org/10.1109/igarss.2015.7326092","url":null,"abstract":"Joint statistical properties of range-resolved sea backscatter at different polarizations are investigated using large Monte Carlo ensembles of numerically generated data. The simulations are based on the first-principles boundary integral equation technique in the two-dimensional (2-D) space and produce noise-free sets of backscatter corresponding to well-defined wind conditions. This study focuses on the joint probability density function (PDF) of vertically (VV) and horizontally (HH) polarized clutter intensities, investigating the behavior of the function's orientation and shape with variations in the incidence angle and wind speed. Comparisons with the results generated using coherent formulation of the popular Two-Scale Model reveal strengths and weaknesses of the latter in reproducing detailed statistical descriptors of sea clutter such as the joint VV-HH PDF.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122449116","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 : 2015-07-26DOI: 10.1109/IGARSS.2015.7325912
F. Yuan, Y. Lee, Y. S. Meng, J. Ong
A new method is proposed in this paper to determine the cloud vertical structure using water vapor pressure estimated from radiosonde profile. The presence of a cloud depends on the following criteria: the measured water vapor pressure being larger than the critical water vapor pressure at the same level. The estimated results of cloud vertical structures using the proposed method are compared with the Salonen and Uppala model and ceilometer (CL31) data. Results show good agreement between the proposed model, the existing model and the measured data.
{"title":"Detection of cloud vertical structure using water vapor pressure in tropical region","authors":"F. Yuan, Y. Lee, Y. S. Meng, J. Ong","doi":"10.1109/IGARSS.2015.7325912","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7325912","url":null,"abstract":"A new method is proposed in this paper to determine the cloud vertical structure using water vapor pressure estimated from radiosonde profile. The presence of a cloud depends on the following criteria: the measured water vapor pressure being larger than the critical water vapor pressure at the same level. The estimated results of cloud vertical structures using the proposed method are compared with the Salonen and Uppala model and ceilometer (CL31) data. Results show good agreement between the proposed model, the existing model and the measured data.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"381 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122777013","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 : 2015-07-26DOI: 10.1109/IGARSS.2015.7326163
A. Salberg
In this paper, we propose an algorithm for automatic detection of seals in aerial remote sensing images using features extracted from a pre-trained deep convolutional neural network (CNN). The method consists of three stages: (i) Detection of potential objects, (ii) feature extraction and (iii) classification of potential objects. The first stage is application dependent, with the aim of detecting all seal pups in the image, with the expense of detecting a large amount of false objects. The second stage extracts generic image features from a local image corresponding to each potential seal detected in the first stage using a CNN trained on the ImageNet database. In the third stage we apply a linear support vector machine to classify the feature vectors extracted in the second stage. The proposed method was demonstrated to an aerial image that contains 84 pups and 128 adult harp seals, and the results show that we are able to detect the seals with high accuracy (2.7% for the adults and 7.3% for the pups). We conclude that deep CNNs trained on the ImageNet database are well suited as a feature extraction module, and using a simple linear SVM, we were able to separate seals from other objects with very high accuracy. We believe that this methodology may be applied to other remote sensing object recognition tasks.
{"title":"Detection of seals in remote sensing images using features extracted from deep convolutional neural networks","authors":"A. Salberg","doi":"10.1109/IGARSS.2015.7326163","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326163","url":null,"abstract":"In this paper, we propose an algorithm for automatic detection of seals in aerial remote sensing images using features extracted from a pre-trained deep convolutional neural network (CNN). The method consists of three stages: (i) Detection of potential objects, (ii) feature extraction and (iii) classification of potential objects. The first stage is application dependent, with the aim of detecting all seal pups in the image, with the expense of detecting a large amount of false objects. The second stage extracts generic image features from a local image corresponding to each potential seal detected in the first stage using a CNN trained on the ImageNet database. In the third stage we apply a linear support vector machine to classify the feature vectors extracted in the second stage. The proposed method was demonstrated to an aerial image that contains 84 pups and 128 adult harp seals, and the results show that we are able to detect the seals with high accuracy (2.7% for the adults and 7.3% for the pups). We conclude that deep CNNs trained on the ImageNet database are well suited as a feature extraction module, and using a simple linear SVM, we were able to separate seals from other objects with very high accuracy. We believe that this methodology may be applied to other remote sensing object recognition tasks.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122791778","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 : 2015-07-26DOI: 10.1109/IGARSS.2015.7326082
Leandro Pralon, Gabriel Vasile, A. Anghel, N. Besic
The multiplicative model, expressed as a product between the square root of a scalar positive quantity (texture) and the description of an equivalent homogeneous surface (speckle), is one of the most disseminated models used to describe high-resolution Polarimetric Synthetic Aperture Radar clutter. Recently, a statistical test was proposed to verify the validity of the model. Within this context, this paper analysis, qualitatively and quantitatively, a P-band airborne dataset acquired by the Office National d'Études et de Recherches Aérospatiales (ONERA) over the French Guiana in 2009 in the frame of the European Space Agency campaign TropiSAR, carefully investigating the regions were the aforementioned does not hold.
{"title":"On the robustness of the ICA based ICTD with respect to the spherical symmetry of the PolSAR data","authors":"Leandro Pralon, Gabriel Vasile, A. Anghel, N. Besic","doi":"10.1109/IGARSS.2015.7326082","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326082","url":null,"abstract":"The multiplicative model, expressed as a product between the square root of a scalar positive quantity (texture) and the description of an equivalent homogeneous surface (speckle), is one of the most disseminated models used to describe high-resolution Polarimetric Synthetic Aperture Radar clutter. Recently, a statistical test was proposed to verify the validity of the model. Within this context, this paper analysis, qualitatively and quantitatively, a P-band airborne dataset acquired by the Office National d'Études et de Recherches Aérospatiales (ONERA) over the French Guiana in 2009 in the frame of the European Space Agency campaign TropiSAR, carefully investigating the regions were the aforementioned does not hold.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122808259","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 : 2015-07-26DOI: 10.1109/IGARSS.2015.7326476
Di Wu, Ye Zhang, Yushi Chen
Hyperspectral images (HSIs) are often contaminated by noise, in order to remove the image noise efficiently and acquire excellent results. We propose a new denoising method based on 3D sparse coding. Firstly, to make full use of spectral information of hyperspectral data, we extract patches from HSIs and each patch contains the same area of different band. Secondly, we use aforementioned method to extract all patches and train these patches, the dictionary can be obtained, further calculate sparse coefficients. Finally, we can restore the HISs through the dictionary and the sparse coefficients. Experiments are implemented using the HSIs collected by AVIRIS and ROSIS. Results indicate that compared with common 2D sparse coding method, 3D sparse method can effectively improve the restoration performance for both subjective visual and objective evaluation criterion.
{"title":"3D sparse coding based denoising of hyperspectral images","authors":"Di Wu, Ye Zhang, Yushi Chen","doi":"10.1109/IGARSS.2015.7326476","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326476","url":null,"abstract":"Hyperspectral images (HSIs) are often contaminated by noise, in order to remove the image noise efficiently and acquire excellent results. We propose a new denoising method based on 3D sparse coding. Firstly, to make full use of spectral information of hyperspectral data, we extract patches from HSIs and each patch contains the same area of different band. Secondly, we use aforementioned method to extract all patches and train these patches, the dictionary can be obtained, further calculate sparse coefficients. Finally, we can restore the HISs through the dictionary and the sparse coefficients. Experiments are implemented using the HSIs collected by AVIRIS and ROSIS. Results indicate that compared with common 2D sparse coding method, 3D sparse method can effectively improve the restoration performance for both subjective visual and objective evaluation criterion.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122653815","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 : 2015-07-26DOI: 10.1109/IGARSS.2015.7326534
Z. Bochenek, D. Ziolkowski, M. Bartold
NOAA AVHRR satellite data were applied for studying relationships between vegetation indices derived from these images and meteorological parameters describing climate changes, in order to assess forest condition. Five forests areas located in various parts of Poland located in various climatic zones, characterized by different tree species were used for this purpose. 15-years database of NOAA AVHRR images and meteorological data was utilized in this work. The conducted study revealed, that there is distinct relation between meteorological situation, described by temperature and precipitation and NDVI index derived from low resolution satellite data. This conclusion is supported with results of ground measurements and high-resolution satellite data analyzed for selected forest areas.
{"title":"Forest condition assessment through analyzing relations between meteorological parameters describing climate changes and vegetation indices derived from low-resolution satellite data","authors":"Z. Bochenek, D. Ziolkowski, M. Bartold","doi":"10.1109/IGARSS.2015.7326534","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326534","url":null,"abstract":"NOAA AVHRR satellite data were applied for studying relationships between vegetation indices derived from these images and meteorological parameters describing climate changes, in order to assess forest condition. Five forests areas located in various parts of Poland located in various climatic zones, characterized by different tree species were used for this purpose. 15-years database of NOAA AVHRR images and meteorological data was utilized in this work. The conducted study revealed, that there is distinct relation between meteorological situation, described by temperature and precipitation and NDVI index derived from low resolution satellite data. This conclusion is supported with results of ground measurements and high-resolution satellite data analyzed for selected forest areas.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"305 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122804068","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 : 2015-07-26DOI: 10.1109/IGARSS.2015.7326765
X. Lin, Fangfang Li, Yueting Zhang, Dadi Meng, Donghui Hu, C. Ding
The precision of the mutilsquint methods effects by several factors. This paper deduces the effects of these factors on the multisquint estimation accuracy. Expression of the estimation accuracy is deduced, which provides theoretical bases for the parameter choice and system design of the airborne interferometric SAR.
{"title":"Study on effect factors of multisquint estimation of time-varying baseline errors in repeat-pass airborne SAR","authors":"X. Lin, Fangfang Li, Yueting Zhang, Dadi Meng, Donghui Hu, C. Ding","doi":"10.1109/IGARSS.2015.7326765","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326765","url":null,"abstract":"The precision of the mutilsquint methods effects by several factors. This paper deduces the effects of these factors on the multisquint estimation accuracy. Expression of the estimation accuracy is deduced, which provides theoretical bases for the parameter choice and system design of the airborne interferometric SAR.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123022384","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 : 2015-07-26DOI: 10.1109/IGARSS.2015.7326643
E. Varona, C. López-Martínez, A. Broquetas
This paper shows the necessity to include the impact of instrument-driven constrains in the physical modeling of polarimetric data from spaceborne SAR missions, specifically for TerraSAR-X (TSX) case. The intrinsic acquisition of fully (quad-pol) polarimetric data, exploiting the dual-receive antenna (DRA) mode, may induce two additional sources of decorrelation when observing challenging dynamic scenarios, such as the ocean or sea: limited signal-to-noise ratio (SNR) as the receive antenna is halved and temporal decor-relation induced over the along-track configuration (spatial baseline between the polarimetic channels) due to internal clutter motion. Experimental quad-pol TSX data over the ocean has been used to study the applicability of the original X-Bragg model as well as its extension accounting for these system/scenario dependent limitations, which shows a much better fitting.
{"title":"Instrument-driven constrains on scattering modeling for TerraSAR-X POLSAR data","authors":"E. Varona, C. López-Martínez, A. Broquetas","doi":"10.1109/IGARSS.2015.7326643","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326643","url":null,"abstract":"This paper shows the necessity to include the impact of instrument-driven constrains in the physical modeling of polarimetric data from spaceborne SAR missions, specifically for TerraSAR-X (TSX) case. The intrinsic acquisition of fully (quad-pol) polarimetric data, exploiting the dual-receive antenna (DRA) mode, may induce two additional sources of decorrelation when observing challenging dynamic scenarios, such as the ocean or sea: limited signal-to-noise ratio (SNR) as the receive antenna is halved and temporal decor-relation induced over the along-track configuration (spatial baseline between the polarimetic channels) due to internal clutter motion. Experimental quad-pol TSX data over the ocean has been used to study the applicability of the original X-Bragg model as well as its extension accounting for these system/scenario dependent limitations, which shows a much better fitting.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114168244","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}
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