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Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
Journal Prestige (SJR): 1.547
Citation Impact (citeScore): 4
Number of Followers: 53  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1939-1404
Published by IEEE Homepage  [191 journals]
  • Frontcover
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • IEEE Geoscience and Remote Sensing Societys
    • Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • IEEE Geoscience and Remote Sensing Societys
    • Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Institutional Listings
    • Abstract: Presents a listing of institutions relevant for this issue of the publication.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Foreword to the Special Issue on Forest Structure Estimation in Remote
    • Authors: P. Dubois-Fernandez;L. Fatoyinbo;I. Hajnsek;S. Saatchi;K. Scipal;
      Pages: 3384 - 3385
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • L- and P-Band 3-D SAR Reflectivity Profiles Versus Lidar Waveforms: The
           AfriSAR Case
    • Authors: Matteo Pardini;Marivi Tello;Victor Cazcarra-Bes;Konstantinos P. Papathanassiou;Irena Hajnsek;
      Pages: 3386 - 3401
      Abstract: The aim of this paper is to compare L- and P-band vertical backscattering profiles estimated by means of synthetic aperture radar (SAR) tomography and full light detection and ranging (lidar) waveforms in terms of their ability to distinguish different tropical forest structure types. The comparison relies on the unique DLR F-SAR and NASA Land, Vegetation and Ice Sensor (LVIS) lidar datasets acquired in 2016 in the frame of the AfriSAR campaign. In particular, F-SAR and LVIS data over three different test sites complemented by plot field measurements are used. First, the SAR and lidar three-dimensional (3-D) datasets are compared and discussed on a qualitative basis. The ability to penetrate into and through the canopy down to the ground is assessed at L- and P-band in terms of both the ground-to-volume power ratio and the performance to estimate the location of the underlying ground. The effect of polarimetry on the visibility of the ground is discussed as well. Finally, the 3-D measurements for each configuration are compared with respect to their ability to derive physical forest structure descriptors. For this, vertical structure indices derived from the volume-only 3-D radar reflectivity at L- and P-band and from the LVIS profiles are compared against each other as well as against plot-derived indices.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Forest Structure Characterization From SAR Tomography at L-Band
    • Authors: Marivi Tello;Victor Cazcarra-Bes;Matteo Pardini;Konstantinos Papathanassiou;
      Pages: 3402 - 3414
      Abstract: Synthetic aperture radar (SAR) remote sensing configurations are able to provide continuous measurements on global scales sensitive to the vertical structure of forests with a high spatial and temporal resolution. Furthermore, the development of tomographic SAR techniques allows the reconstruction of the three-dimensional (3-D) radar reflectivity opening the door for 3-D forest monitoring. However, the link between 3-D radar reflectivity and 3-D forest structure is not yet established. In this sense, this paper introduced a framework that allows a qualitative and quantitative interpretation of physical forest structure from tomographic SAR data at L-band. For this, forest structure is parameterized into a set of a horizontal and a vertical structure index. From inventory data, both indices can be derived from the spatial distribution and the dimensions of the trees. Similarly, two structure indices are derived from the 3-D spatial distribution of the local maxima of the reconstructed 3-D radar reflectivity profiles at L-band. The proposed methodology is tested by means of experimental tomographic L-band data acquired over the temperate forest site of Traunstein in Germany. The obtained horizontal and vertical structure indices are validated against the corresponding estimates obtained from inventory measurements and against the same indices derived from the vertical profiles of airborne Lidar data. The high correlation between the forest structure indices obtained from these three different data sources (expressed by correlation coefficients between 0.75 and 0.87) indicates the potential of the proposed framework.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Forest Height Estimation Using Multibaseline PolInSAR and Sparse Lidar
           Data Fusion
    • Authors: Michael Denbina;Marc Simard;Brian Hawkins;
      Pages: 3415 - 3433
      Abstract: We demonstrate a method using lidar data fusion to improve the forest height estimation accuracy of multibaseline polarimetric synthetic aperture radar interferometry (PolInSAR). Compared to single-baseline PolInSAR, multibaseline PolInSAR allows forest canopy height to be estimated more accurately across a wider range of height values. However, to arrive at a single forest height estimate, the estimates from the multiple baselines must be selected or weighted. A number of approaches to selecting between baselines have been proposed in the literature, but they are generally based on simple metrics of the PolInSAR data and do not necessarily capture the full range of characteristics that make one baseline produce more accurate forest height estimates than another. We solve this problem by treating baseline selection as a supervised classification problem that can be trained using a small amount of sparse lidar data located within the PolInSAR coverage area. We train a support vector machine classifier using a variety of coarse lidar sample spacings of 250 m and greater, to demonstrate that data from future spaceborne lidar missions will be sufficient for this purpose. We demonstrate results for multiple study areas in the country of Gabon using data collected by NASA's uninhabited aerial vehicle synthetic aperture radar and land, vegetation, and ice sensor lidar. The use of lidar fusion for PolInSAR baseline selection yields improved results compared to standard baseline selection methods, and further demonstrates the strong potential of PolInSAR and lidar fusion for remote sensing of forests.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Multibaseline TanDEM-X Mangrove Height Estimation: The Selection of the
           Vertical Wavenumber
    • Authors: Seung-Kuk Lee;Temilola E. Fatoyinbo;David Lagomasino;Emanuelle Feliciano;Carl Trettin;
      Pages: 3434 - 3442
      Abstract: We generated a large-scale mangrove forest height map using multiple TanDEM-X (TDX) interferometric synthetic aperture radar (InSAR) acquisitions with various spatial baselines in order to improve the height estimation accuracy across a wide range of forest heights. The forest height inversion using InSAR data is strongly dependent upon the vertical wavenumber (i.e., perpendicular baseline). First, we investigated the role of the vertical wavenumber in forest height inversion from InSAR data using the sensitivity of the interferometric (volume) coherence to forest height. We used corrected but lower resolution and accuracy Shuttle Radar Topography Mission (SRTM) mangrove height maps as a priori information over Akanda and Pongara National Parks in Gabon to estimate lower and upper boundaries of the vertical wavenumber over test sites from the measured coherence-to-height sensitivity. Only TDX acquisitions within the boundaries of the vertical wavenumber were selected and combined for multibaseline mangrove height inversion. Mangrove forest height was obtained with multibaseline TDX acquisitions and was validated against the reference height derived from field measurement data providing improvements in multibaseline inversion over existing height estimates (i.e., SRTM height) and single-baseline inversions (multibaseline inversion: $r^{2}= {text{0.98}}$, root mean square error (RMSE) of 2.73 m; SRTM height: $r^{2}= {text{0.86}}$ , ${text{RMSE}}= {text{7.21}}$  m; single-baseline inversions: $r^{2}= {text{0.08}}{hbox{-}} {text{0.97}}$, ${text{RMSE}}= {text{3.86}}$ –11.10 m). As a result, to accurately estimate forest heights over a wide range (3–60 m), multibaseline InSAR acquisitions (at least three different baselines) are needed to exclude biases associated with the vertical wavenumber in forest height inversion.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Radar Forest Height Estimation in Mountainous Terrain Using Tandem-X
           Coherence Data
    • Authors: Hao Chen;Shane R. Cloude;David G. Goodenough;David A. Hill;Andrea Nesdoly;
      Pages: 3443 - 3452
      Abstract: In this paper, we consider the problem of radar estimation of forest canopy height in regions with dense forests and severe topography. We combine a reference digital elevation model with multiple satellite baselines from ascending and descending orbits to develop a merging algorithm relating single pass interferometric coherence to forest canopy height. We first describe the algorithm and processing steps used for height estimation and then apply the technique to a mountainous study site in British Columbia, Canada, using data from the Tandem-X satellite pair. We devise a new masking scheme to isolate potential problem areas in sloped terrain and apply the new merging algorithm by using multiple Tandem-X tracks to overcome the gaps left due to the masking procedure. The radar height products are validated by using a network of ground forest measurement sites and supporting lidar. The regression statistics show an r2 of 0.70 and rmse of 4.1 m between the radar and the field measured heights. By examining height errors, we implement a new test for the presence of canopy extinction, or subcanopy surface scattering, and demonstrate that in the dense and mountainous forests of British Columbia, there are significant canopy extinction effects in X-band imagery.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • A Machine-Learning Approach to PolInSAR and LiDAR Data Fusion for Improved
    • Authors: Maryam Pourshamsi;Mariano Garcia;Marco Lavalle;Heiko Balzter;
      Pages: 3453 - 3463
      Abstract: This paper investigates the benefits of integrating multibaseline polarimetric interferometric SAR (PolInSAR) data with LiDAR measurements using a machine-learning approach in order to obtain improved forest canopy height estimates. Multiple interferometric baselines are required to ensure consistent height retrieval performance across a broad range of tree heights. Previous studies have proposed multibaseline merging strategies using metrics extracted from PolInSAR measurements. Here, we introduce the multibaseline merging using a support vector machine trained by sparse LiDAR samples. The novelty of this method lies in the new way of combining the two datasets. Its advantage is that it does not require a complete LiDAR coverage, but only sparse LiDAR samples distributed over the PolInSAR image. LiDAR samples are not used to obtain the best height among a set of height stacks, but rather to train the retrieval algorithm in selecting the best height using the variables derived through PolInSAR processing. This enables more accurate height estimation for a wider scene covered by the SAR with only partial LiDAR coverage. We test our approach on NASA AfriSAR data acquired over tropical forests by the L-band UAVSAR and the LVIS LiDAR instruments. The estimated height from this approach has a higher accuracy (r2 = 0.81, RMSE = 7.1 m) than previously introduced multibaseline merging approach (r2 = 0.67, RMSE = 9.2 m). This method is beneficial to future spaceborne missions, such as GEDI and BIOMASS, which will provide a wealth of near-contemporaneous LiDAR samples and PolInSAR measurements for mapping forest structure at global scale.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Estimating Tree Heights Using Multibaseline PolInSAR Data With
           Compensation for Temporal Decorrelation, Case Study: AfriSAR Campaign Data
    • Authors: Nafiseh Ghasemi;Valentyn A. Tolpekin;Alfred Stein;
      Pages: 3464 - 3477
      Abstract: This paper presents a multibaseline method to increase the accuracy of height estimation when using SAR tomographic data. It is based upon mitigating the temporal decorrelation induced by wind. The Fourier–Legendre function of different orders was fitted to each pixel as the structure function in the PCT model. It was combined with the motion standard deviation function from the random-motion-over ground (RMoG) model. L-band multibaseline data are used that were acquired during the AfriSAR campaign over La Lope National Park in Gabon with a height range between 0 and 60 m that has an average of 30 m and standard deviation of 15 m. The results were compared with those from the regular PCT model using the root mean square error (RMSE). Histograms were compared to the one obtained from Lidar height map. The average RMSE was equal to 7.5 m for the regular PCT model and to 5.6 m for the modified PCT model. We concluded that the accuracy of tree height estimation increased after modeling of temporal decorrelation. This is of value for future satellite missions that would collect tomographic data over forest areas.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Uncertainties in Forest Canopy Height Estimation From Polarimetric
           Interferometric SAR Data
    • Authors: Bryan Riel;Michael Denbina;Marco Lavalle;
      Pages: 3478 - 3491
      Abstract: The random volume over ground (RVoG) model has been widely applied to estimate forest tree height from polarimetric synthetic aperture radar (SAR) interferometry (PolInSAR) data for the past two decades. Successful application of the RVoG model requires certain assumptions to be valid for the imaged forest and the acquisition scenarios in order to avoid large errors in height estimates. Quantification of errors and uncertainties of RVoG-estimated heights have typically been limited to comparison against external validation data, such as lidar or field measurements. In this paper, we present a straightforward approach to simultaneously estimate height and height uncertainty from PolInSAR data using a Bayesian framework that accounts for errors in the data, as well as errors due to incorrect RVoG modeling assumptions, such as those caused by temporal decorrelation effects and errors in ground phase estimation. We apply our method to synthetic data to study how forest height uncertainty depends on modeling assumptions and PolInSAR acquisition parameters. We also compare our estimated Bayesian uncertainties to PolInSAR-derived and lidar-derived height RMS deviations observed over Gabonese tropical forests during the joint NASA-ESA 2016 AfriSAR campaign. Our results show good correspondence between uncertainties and deviations, as well as a strong correlation between uncertainty and estimated tree height. Furthermore, we demonstrate that we can associate specific areas of high uncertainty to confounding effects, such as temporal decorrelation and noncanopy related scattering.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • The AfriSAR Campaign: Tomographic Analysis With Phase-Screen Correction
           for P-Band Acquisitions
    • Authors: Valentine Wasik;Pascale C. Dubois-Fernandez;Cédric Taillandier;Sassan S. Saatchi;
      Pages: 3492 - 3504
      Abstract: ESA's earth explorer BIOMASS mission is a P-band (432–438 MHz) synthetic aperture radar (SAR) using a combination of polarimetry and interferometric observations to quantify the vertical structure and biomass of global forests, with the primary focus on tropical forests. The methodology to map the vertical structure of the forest is based on multibaseline tomographic measurements from space. In this paper, we use data acquired by airborne sensors during the AfriSAR campaign in humid tropical forests of Africa to examine the potential of P -band tomographic SAR measurements in estimating forest parameters. We use data acquired by ONERA's P-band SAR system over the Lopé National Park in central Gabon during July 2015 to estimate vertical profiles. In processing the multibaseline data, we develop and implement a phase-screen correction methodology based on recent works by Tebaldini et al. to improve the quality of measurements by removing phase perturbations associated with platform motions and uncertainties in flight trajectories. The vertical structure estimated from the corrected tomographic measurements are then compared with small and large footprint light detection and ranging (Lidar) observations collected as part of the AfriSAR campaign. The results suggest that phase-screen correction can significantly improve the vertical profile of radar backscattered power to match the Lidar observations in detecting ground, vertical vegetation density, and total height of the forests across a variety of forest types and terrain complexity.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • An Empirical Study on the Impact of Changing Weather Conditions on
           Repeat-Pass SAR Tomography
    • Authors: Yu Bai;Stefano Tebaldini;Dinh Ho Tong Minh;Wen Yang;
      Pages: 3505 - 3511
      Abstract: Researches carried out in the last years have shown that the use of P-band SAR tomography (TomoSAR) largely improves the retrieval of the above-ground biomass (AGB) in tropical forests, providing a most encouraging element toward the systematic employment of TomoSAR techniques in the frame of the upcoming spaceborne mission BIOMASS. All of these researches were carried out using campaign data acquired in a single day, and under stable (mostly sunny) weather conditions. The impact of temporal decorrelation was considered in the literature by analyzing ground-based Radar data from the TropiSCAT campaign (Paracou, French Guiana), and found not to be a show-stopper for BIOMASS tomography. Yet, the validity of this analysis was limited to sunny days only. Accordingly, a precise assessment of the impact of changing weather conditions on TomoSAR is currently missing. The aim of this paper is to provide a first experimental element to fill this gap. To do this, data from the TropiSCAT archive are reprocessed to mimic BIOMASS repeat-pass tomography. Since BIOMASS tomography will be implemented by taking seven acquisitions with a revisit time of three days, we form tomograms by taking two TropiSCAT antennas every three days (and three antennas on the last day), which means that any single tomogram is actually obtained by mixing seven different days and under different weather conditions. The quality of tomographic imaging is then assessed by evaluating the observed backscattered power fluctuations in the tomogram time series. While imaging quality is observed to degrade by mixing different days, the resulting temporal variations of the backscattered power in the canopy layer are within 1.5-dB rms in cross polarization. For this forest site, this error is translated into an AGB error of about 50-100 t/ha, which is 20% or less of forest AGB.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Comparison of Small- and Large-Footprint Lidar Characterization of
           Tropical Forest Aboveground Structure and Biomass: A Case Study From
           Central Gabon
    • Authors: Carlos Alberto Silva;Sassan Saatchi;Mariano Garcia;Nicolas Labrière;Carine Klauberg;António Ferraz;Victoria Meyer;Kathryn J. Jeffery;Katharine Abernethy;Lee White;Kaiguang Zhao;Simon L. Lewis;Andrew T. Hudak;
      Pages: 3512 - 3526
      Abstract: NASA's Global Ecosystem Dynamic Investigation (GEDI) mission has been designed to measure forest structure using lidar waveforms to sample the earth's vegetation while in orbit aboard the International Space Station. In this paper, we used airborne large-footprint (LF) lidar measurements to simulate GEDI observations from which we retrieved ground elevation, vegetation height, and aboveground biomass (AGB). GEDI-like product accuracy was then assessed by comparing them to similar products derived from airborne small-footprint (SF) lidar measurements. The study focused on tropical forests and used data collected during the NASA and European Space Agency (ESA) AfriSAR ground and airborne campaigns in the Lope National Park in Central Gabon. The measurements covered a gradient of successional stages of forest development with different height, canopy density, and topography. The comparison of the two sensors shows that LF lidar waveforms and simulated waveforms from SF lidar are equivalent in their ability to estimate ground elevation (RMSE = 0.5 m, bias = 0.29 m) and maximum forest height (RMSE = 2.99 m, bias = 0.24 m) over the study area. The difference in the AGB estimated from both lidar instruments at the 1-ha spatial scale is small over the entire study area (RMSE = 6.34 Mg·ha −1, bias = 11.27 Mg·ha−1) and the bias is attributed to the impact of ground slopes greater than 10–20° on the LF lidar measurements of forest height. Our results support the ability of GEDILF lidar to measure the complex structure of humid tropical forests and provide AGB estimates comparable t- SF-derived ones.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Improved Biomass Calibration and Validation With Terrestrial LiDAR:
           Implications for Future LiDAR and SAR Missions
    • Authors: Atticus E. L. Stovall;Herman H. Shugart;
      Pages: 3527 - 3537
      Abstract: Future NASA and ESA satellite missions plan to better quantify global carbon stocks through detailed observations of forest structure, but ultimately rely on uncertain ground measurement approaches for calibration and validation. A substantial amount of uncertainty in estimating plot-level biomass can be attributed to inadequate and unrepresentative allometric relationships used to convert plot-level tree measurements to estimates of aboveground biomass. These allometric equations are known to have high errors and biases, particularly in carbon-rich forests, because they were calibrated with small and often biased samples of destructively harvested trees. To overcome this issue, we present and test a framework for nondestructively estimating tree and plot-level biomass with terrestrial laser scanning (TLS). We modeled 243 trees from 12 species with TLS and created ten low-RMSE allometric equations. The full 3-D reconstructions, TLS allometry, and Jenkins et al. (2003) allometry were used to calibrate SAR- and LiDAR-based empirical biomass models to investigate the potential for improved accuracy and reduced uncertainty. TLS reduced plot-level RMSE from 18.5% to 9.8% and revealed a systematic negative bias in the national equations. At the calibration stage, allometric uncertainty accounted for 2.8–28.4% of the total RMSE, increasing in relative contribution as calibration improved with sensor fusion. Our findings suggest that TLS plot acquisitions and nondestructive allometry can play a vital role for reducing uncertainty in calibration and validation data for biomass mapping in the upcoming NASA and ESA missions.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Assessment of a Power Law Relationship Between P-Band SAR Backscatter and
           Aboveground Biomass and Its Implications for BIOMASS Mission Performance
    • Authors: Michael Schlund;Klaus Scipal;Shaun Quegan;
      Pages: 3538 - 3547
      Abstract: This paper presents an analysis of a logarithmic relationship between P-band cross-polarized backscatter from synthetic aperture radar (SAR) and aboveground biomass (AGB) across different forest types based on multiple airborne datasets. It is found that the logarithmic function provides a statistically significant fit to the observed relationship between HV backscatter and AGB. While the coefficient of determination varies between datasets, the slopes, and intercepts of many of the models are not significantly different, especially when similar AGB ranges are assessed. Pooled boreal and pooled tropical data have slopes that are not significantly different, but they have different intercepts. Using the power law formulation of the logarithmic relation allows estimation of both the equivalent number of looks (ENL) needed to retrieve AGB with a given uncertainty and the sensitivity of the AGB inversion. The campaign data indicates that boreal forests require a larger ENL than tropical forests to achieve a specified relative accuracy. The ENL can be increased by multichannel filtering, but ascending and descending images will need to be combined to meet the performance requirements of the BIOMASS mission. The analysis also indicates that the relative change in AGB associated with a given backscatter change depends only on the magnitude of the change and the exponent of the power law, and further implies that to achieve a relative AGB accuracy of 20% or better, residual errors from radiometric distortions produced by the system and environmental effects must not exceed 0.43 dB in tropical and 0.39 dB in boreal forests.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Modeling and Detection of Deforestation and Forest Growth in Multitemporal
           TanDEM-X Data
    • Authors: Maciej J. Soja;Henrik J. Persson;Lars M. H. Ulander;
      Pages: 3548 - 3563
      Abstract: This paper compares three approaches to forest change modeling in multitemporal (MT) InSAR data acquired with the X-band system TanDEM-X over a forest with known topography. Volume decorrelation is modeled with the two-level model (TLM), which describes forest scattering using two parameters: forest height $h$ and vegetation scattering fraction $zeta$, accounting for both canopy cover and electromagnetic scattering properties. The single-temporal (ST) approach allows both $h$ and $zeta$ to change between acquisitions. The MT approach keeps $h$ constant and models all change by varying $zeta$ . The MT growth (MTG) approach is based on MT, but it accounts for height growth by letting $h$ have a constant annual increase. Monte Carlo simulations show that MT is more robust than ST with respect to coherence and phase calibration errors and height estimation ambiguities. All three inversion approaches are also applied to 12 VV-polarized TanDEM-X acquisitions made during the summers of 2011–2014 over Remningstorp, a hemiboreal forest in southern Sweden. MT and MTG show better height estimation performance than ST, and MTG provides more consistent canopy cover estimates than MT. For MTG, the root-mean-square difference is 1.1 m (6.6%; $r=0.92$) for forest height and 0.16 (22%; $r=0.48$) for canopy cover, compared with similar metrics from airborne lidar scanning (ALS). The annual height increase estimated with MTG is found correlated with a related ALS metric, although a bias is observed. A deforestation detection method is proposed, correctly detecting 15 out of 19 areas with canopy cover loss above 50%.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Temporal Survey of P- and L-Band Polarimetric Backscatter in Boreal
    • Authors: Albert R. Monteith;Lars M. H. Ulander;
      Pages: 3564 - 3577
      Abstract: Environmental conditions and seasonal variations affect the backscattered radar signal from a forest. This potentially causes errors in a biomass retrieval scheme using data from the synthetic aperture radar (SAR) data. A better understanding of these effects and the electromagnetic scattering mechanisms in forests is required to improve biomass estimation algorithms for current and upcoming P- and L-band SAR missions. In this paper, temporal changes in HH-, VV-, and HV-polarized P- and L-band radar backscatter and temporal coherence from a boreal forest site are analyzed in relation to environmental parameters. The radar data were collected from a stand of mature Norway spruce ( Picea abies (L.) Karst.) with an above-ground biomass of approximately 250 tons/ha at intervals of 5 min from January to August 2017 using the BorealScat tower-based scatterometer. It was observed that subzero temperatures during the winters cause large variations (4 to 10 dB) in P- and L-band backscatter, for which the HH/VV backscatter ratio offered some mitigation. High wind speeds were also seen to cause deviations in the average backscatter at P-band due to decreased double-bounce scattering. Severe temporal decorrelation was observed at L-band over timescales of days or more, whereas the P-band temporal coherence remained high ( $>$0.9) for at least a month neglecting windy periods. Temporal coherence at P-band was highest during night times when wind speeds are low.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Mapping Three-Dimensional Structures of Forest Canopy Using UAV Stereo
           Imagery: Evaluating Impacts of Forward Overlaps and Image Resolutions With
           LiDAR Data as Reference
    • Authors: Wenjian Ni;Guoqing Sun;Yong Pang;Zhiyu Zhang;Jianli Liu;Aqiang Yang;Yao Wang;Dafeng Zhang;
      Pages: 3578 - 3589
      Abstract: The application of aerial stereo imagery on the measurement of forest three-dimensional structures is growing in recent years due to the rapid development of unmanned aerial vehicle platforms and automatic processing algorithms of stereo images. Yet there is still no clear knowledge about how the description of forest three-dimensional structures is affected by settings of critical acquisition parameters of stereo images. This study systematically addressed the impacts of image resolutions and forward overlaps over a broad range by using LiDAR data as reference. The different combinations of image resolutions and forward overlaps used in this study are produced by image average downsampling and subsetting. Their performances were evaluated from four aspects, including computation loads, point densities, estimation of canopy height indices at forest stand level, and the vertical distribution of point clouds over forest stands and along forest transects with different levels of canopy closure. Results showed that the coupling between image resolutions and forward overlaps in the data processing should be given full consideration. Generally, finer image resolutions require higher forward overlaps; otherwise, much of the area could not be detected and sparse trees were easily missed. It was a better choice to degrade image resolutions while keeping forward overlap in data processing if more blank areas appeared or sparse trees could not be detected. The good match between image resolutions and forward overlaps could dramatically reduce the computation load while keeping the estimation accuracy.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Estimation of Canopy Height Using an Airborne Ku-Band Frequency-Modulated
           Continuous Waveform Profiling Radar
    • Authors: Hui Zhou;Yuwei Chen;Juha Hyyppä;Ziyi Feng;Fashuai Li;Teemu Hakala;Xinmin Xu;Xiaolei Zhu;
      Pages: 3590 - 3597
      Abstract: An airborne Ku-band frequency-modulated continuous waveform (FMCW) profiling radar terms as Tomoradar provides a distance-resolved measure of microwave radiation backscattered from the canopy surface and the underlying ground. The Tomoradar waveform data are acquired in the southern Boreal Forest Zone with Scots pine, Norway spruce, and birch as major species in Finland. A weighted filtering algorithm based on statistical properties of noise is designed to process the original waveform. In addition, another algorithm of estimating canopy height for the processed waveform is developed by extracting the canopy top and ground position. A higher-precision reference data from a Velodyne VLP-16 laser scanner and a digital terrain model are introduced to validate the accuracy of extracted canopy height. According to the processed results from 127 765 copolarization measurements in 32 stripes of Tomoradar field test, the mean error of canopy height varies from −0.04 to 1.53 m, and the root-mean-square error approximates 1 m. Moreover, the estimated canopy heights highly correlate with the reference data in view of that the correlation coefficients maintain from 0.86 to 0.99 with an average value of 0.96. All these results demonstrate that Tomoradar presents an important approach in estimating the canopy height with several meters footprint and is feasible of being a validation instrument for satellite LiDAR with large footprint in the forest inventory.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Quantitative Assessment of Scots Pine (Pinus Sylvestris L.) Whorl
           Structure in a Forest Environment Using Terrestrial Laser Scanning
    • Authors: Jiri Pyörälä;Xinlian Liang;Mikko Vastaranta;Ninni Saarinen;Ville Kankare;Yunsheng Wang;Markus Holopainen;Juha Hyyppä;
      Pages: 3598 - 3607
      Abstract: State-of-the-art technology available at sawmills enables measurements of whorl numbers and the maximum branch diameter for individual logs, but such information is currently unavailable at the wood procurement planning phase. The first step toward more detailed evaluation of standing timber is to introduce a method that produces similar wood quality indicators in standing forests as those currently used in sawmills. Our aim was to develop a quantitative method to detect and model branches from terrestrial laser scanning (TLS) point clouds data of trees in a forest environment. The test data were obtained from 158 Scots pines (Pinus sylvestris L.) in six mature forest stands. The method was evaluated for the accuracy of the following branch parameters: Number of whorls per tree and for every whorl, the maximum branch diameter and the branch insertion angle associated with it. The analysis concentrated on log-sections (stem diameter>15 cm) where the branches most affect wood's value added. The quantitative whorl detection method had an accuracy of 69.9% and a 1.9% false positive rate. The estimates of the maximum branch diameters and the corresponding insertion angles for each whorl were underestimated by 0.34 cm (11.1%) and 0.67° (1.0%), with a root-mean-squared error of 1.42 cm (46.0%) and 17.2° (26.3%), respectively. Distance from the scanner, occlusion, and wind were the main external factors that affect the method's functionality. Thus, the completeness and point density of the data should be addressed when applying TLS point cloud based tree models to assess branch parameters.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Detection and Geolocation of P-Band Radio Frequency
           Interference Using EcoSAR
    • Authors: Tobias Bollian;Batuhan Osmanoglu;Rafael F. Rincon;Seung-Kuk Lee;Temilola E. Fatoyinbo;
      Pages: 3608 - 3616
      Abstract: The high penetration of P-band signals (300-600 MHz) into dense vegetation and the higher temporal stability at low frequencies are key advantages for the estimation of forest properties using synthetic aperture radar (SAR). However, existing services at those frequencies make P-band SAR imagers more vulnerable to the radio frequency interference (RFI). In this paper, a method to detect and geolocate the RFI using digital beamforming (DBF) is presented. The method is implemented using NASA's EcoSAR measurements. This P-band multichannel radar uses a sniffing pulse interleaved during the DBF SAR operation to sense the RFI. RFI detection is implemented with time-bandpass filters while DBF is used to estimate the angle-of-arrival and geolocate the interference. The method is demonstrated for an interferer how EcoSAR could be used to assess the RFI threats to spaceborne missions.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • In Situ Reference Datasets From the TropiSAR and AfriSAR Campaigns in
           Support of Upcoming Spaceborne Biomass Missions
    • Authors: Nicolas Labrière;Shengli Tao;Jérôme Chave;Klaus Scipal;Thuy Le Toan;Katharine Abernethy;Alfonso Alonso;Nicolas Barbier;Pulchérie Bissiengou;Tânia Casal;Stuart J. Davies;Antonio Ferraz;Bruno Hérault;Gaëlle Jaouen;Kathryn J. Jeffery;David Kenfack;Lisa Korte;Simon L. Lewis;Yadvinder Malhi;Hervé R. Memiaghe;John R. Poulsen;Maxime Réjou-Méchain;Ludovic Villard;Grégoire Vincent;Lee J. T. White;Sassan Saatchi;
      Pages: 3617 - 3627
      Abstract: Tropical forests are a key component of the global carbon cycle. Yet, there are still high uncertainties in forest carbon stock and flux estimates, notably because of their spatial and temporal variability across the tropics. Several upcoming spaceborne missions have been designed to address this gap. High-quality ground data are essential for accurate calibration/validation so that spaceborne biomass missions can reach their full potential in reducing uncertainties regarding forest carbon stocks and fluxes. The BIOMASS mission, a P-band SAR satellite from the European Space Agency (ESA), aims at improving carbon stock mapping and reducing uncertainty in the carbon fluxes from deforestation, forest degradation, and regrowth. In situ activities in support of the BIOMASS mission were carried out in French Guiana and Gabon during the TropiSAR and AfriSAR campaigns. During these campaigns, airborne P-band SAR, forest inventory, and lidar data were collected over six study sites. This paper describes the methods used for forest inventory and lidar data collection and analysis, and presents resulting plot estimates and aboveground biomass maps. These reference datasets along with intermediate products (e.g., canopy height models) can be accessed through ESA's Forest Observation System and the Dryad data repository and will be useful for BIOMASS but also to other spaceborne biomass missions such as GEDI, NISAR, and Tandem-L for calibration/validation purposes. During data quality control and analysis, prospects for reducing uncertainties have been identified, and this paper finishes with a series of recommendations for future tropical forest field campaigns to better serve the remote sensing community.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • A New Cloud Detection Method Supported by GlobeLand30 Data Set
    • Authors: Lin Sun;Xueying Zhou;Jing Wei;Quan Wang;Xinyan Liu;Meiyan Shu;Tingting Chen;Yulei Chi;Wenhua Zhang;
      Pages: 3628 - 3645
      Abstract: In terms of traditional threshold methods, uniform thresholds are used for cloud detection based on remote sensing images; however, due to complex surface structures and cloud conditions, such an approach is typically difficult to effectively implement for high-precision cloud detection. To solve this problem, a new cloud detection algorithm is proposed based on global land cover data. Specifically, a high spatial-resolution at 30-m Global Land Cover Data set with global coverage was employed as background data for image inversions, which further supported cloud detection in remote sensing images. Notably, threshold settings can be varied for different land cover types. Such an algorithm can effectively improve the accuracy of cloud pixel identification for thin and broken clouds, even over bright areas. Moreover, Landsat 5 data are used to perform cloud detection experiments based on this algorithm. The thresholds are considering land cover variations. The thresholds of land cover types spatiotemporally vary, such as vegetation, differed by latitude and over time. In addition, six common land cover types are selected for cloud detection experiments. Then, validations analyses are conducted through visual interpretation and the results indicated that the algorithm is capable of achieving a high cloud detection accuracy. Specifically, the overall RMSE of cloud cover is 4.44%, and the accuracies of cloud and clear-sky pixel identifications is 86.5% and 98.7%, respectively.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Characterizing a Sea Turtle Developmental Habitat Using Landsat
           Observations of Surface-Pelagic Drift Communities in the Eastern Gulf of
    • Authors: Robert F. Hardy;Chuanmin Hu;Blair Witherington;Brian Lapointe;Anne Meylan;Ernst Peebles;Leo Meirose;Shigetomo Hirama;
      Pages: 3646 - 3659
      Abstract: Compared with our understanding of most aspects of sea turtle biology, knowledge of the surface-pelagic juvenile life stages remains limited. Young North Atlantic cheloniids (hard-shelled sea turtles) are closely associated with surface-pelagic drift communities (SPDCs), which are dominated by macroalgae of the genus Sargassum. We quantified SPDCs in the eastern Gulf of Mexico, a region that hosts four species of cheloniids during their surface-pelagic juvenile stage. Landsat satellite imagery was used to identify and measure the areal coverage of SPDCs in the eastern Gulf during 2003–2011 (1323 images). Although the SPDC coverage varied annually, seasonally, and spatially, SPDCs were present year-round, with an estimated mean area of SPDC in each Landsat image of 4.9 km2 (SD = 10.1). The area of SPDCs observed was inversely proportional to sea-surface wind velocity (Spearman's r = −0.33, p < 0.001). The SPDC coverage was greatest during 2005, 2009, and 2011 and least during 2004 and 2010, but the 2010 analysis was affected by the Deepwater Horizon oil spill, which occurred within the study region. In the eastern Gulf, the area of SPDC peaked during June–August of each year. Although the SPDC coverage appeared lower in the eastern Gulf than in other regions of the Gulf and the North Atlantic, surface-pelagic juvenile green, hawksbill, Kemp's ridley, and loggerhead turtles were found to be using this habitat, suggesting that eastern Gulf SPDCs provide developmental habitats that are critical to the recovery of four sea turtle species.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Estimating One-Minute Rain Rate Distributions in the Tropics From TRMM
           Satellite Data (October 2017)
    • Authors: Geraldine Rangmoen Rimven;Kevin S. Paulson;Timothy Bellerby;
      Pages: 3660 - 3667
      Abstract: Internationally recognized prognostic models of rain fade on terrestrial and Earth-space extremely high frequency (EHF) links rely fundamentally on distributions of 1-min rain rates. In Rec. ITU-R P.837-6, these distributions are estimated from the data provided by Numerical Weather Products (NWPs). NWP yields rain accumulations over regions typically larger than 100 km across and over intervals of 6 h. Over the tropics, the Tropical Rain Measuring Mission (TRMM) satellite data yield instantaneous rain rates over regions 5 km across. This paper uses TRMM data to estimate rain rate distributions for telecommunications regulation over the tropics. Rain rate distributions are calculated for each 1° square between 35° south to 35° north. These distributions of instantaneous rain rates over 5 km squares are transformed to distributions over 1 km squares using a correction calculated from U.K. Nimrod radar data. Results are compared to rain distributions in DBSG3, the database of ITU-R Study Group 3. A comparison with the new Rec. ITU-R P.837-7 is also presented. A table of 0.01% exceeded rain rates over the tropics is provided as associated data.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • The Added Value of the VH/VV Polarization-Ratio for Global Soil Moisture
           Estimations From Scatterometer Data
    • Authors: Felix Greifeneder;Claudia Notarnicola;Sebastian Hahn;Mariette Vreugdenhil;Christoph Reimer;Emanuele Santi;Simonetta Paloscia;Wolfgang Wagner;
      Pages: 3668 - 3679
      Abstract: The successor to the current series of Metop advanced scatterometers (ASCATs), the Metop-SG SCA, will be able to record data in dual-polarization, at C-band. The aim of this study is to investigate if the information contained in the added cross-polarization measurements can improve the vegetation parameterization for the estimation of the soil moisture content. In case of the operational Hydrology Satellite Application Facility Metop ASCAT soil moisture product, vegetation dynamics are characterized by the relationship between radar backscattering intensity and the incidence angle, the so-called SLOPE parameter. Building on findings from previous studies, the assumption is that the polarization ratio, i.e., VH/VV, could improve this characterization. To verify this assumption, flexible approaches, able to integrate a combination of ASCAT VV data and AQUARIUS (NASA) VH/VV data were required. Two machine learning methods were chosen: Support-vector-regression and artificial-neural-networks, and one statistical approach, the Bayesian-Regression. Each of these methods were used to derive models with different input configurations, with and without characterization of vegetation. The results show that the information contained in the SLOPE parameter and in PR are similar. Based on a global average, almost identical SMC retrieval accuracies were achieved. Despite that, analysis of the temporal dynamics of SLOPE and PR revealed certain location specific differences, which affect the spatial distribution of SMC retrieval accuracies. As a result, improvements based on the combination of the two parameters are minor overall, but they can be significant locally.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Building-A-Nets: Robust Building Extraction From High-Resolution Remote
           Sensing Images With Adversarial Networks
    • Authors: Xiang Li;Xiaojing Yao;Yi Fang;
      Pages: 3680 - 3687
      Abstract: With the proliferation of high-resolution remote sensing sensor and platforms, vast amounts of aerial image data are becoming easily accessed. High-resolution aerial images provide sufficient structural and texture information for image recognition while also raise new challenges for existing segmentation methods. In recent years, deep neural networks have gained much attention in remote sensing field and achieved remarkable performance for high-resolution remote sensing images segmentation. However, there still exists spatial inconsistency problems caused by independently pixelwise classification while ignoring high-order regularities. In this paper, we developed a novel deep adversarial network, named Building-A-Nets, that jointly trains a deep convolutional neural network (generator) and an adversarial discriminator network for the robust segmentation of building rooftops in remote sensing images. More specifically, the generator produces pixelwise image classification map using a fully convolutional DenseNet model, whereas the discriminator tends to enforce forms of high-order structural features learned from ground-truth label map. The generator and discriminator compete with each other in an adversarial learning process until the equivalence point is reached to produce the optimal segmentation map of building objects. Meanwhile, a soft weight coefficient is adopted to balance the operation of the pixelwise classification and high-order structural feature learning. Experimental results show that our Building-A-Net can successfully detect and rectify spatial inconsistency on aerial images while archiving superior performances compared to other state-of-the-art building extraction methods. Code is available at
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Multilevel Building Detection Framework in Remote Sensing Images Based on
           Convolutional Neural Networks
    • Authors: Yibo Liu;Zhenxin Zhang;Ruofei Zhong;Dong Chen;Yinghai Ke;Jiju Peethambaran;Chuqun Chen;Lan Sun;
      Pages: 3688 - 3700
      Abstract: In this paper, we propose a hierarchical building detection framework based on deep learning model, which focuses on accurately detecting buildings from remote sensing images. To this end, we first construct the generation model of the multilevel training samples using the Gaussian pyramid technique to learn the features of building objects at different scales and spatial resolutions. Then, the building region proposal networks are put forward to quickly extract candidate building regions, thereby increasing the efficiency of the building object detection. Based on the candidate building regions, we establish the multilevel building detection model using the convolutional neural networks (CNNs), from which the generic image features of each building region proposal are calculated. Finally, the obtained features are provided as inputs for training CNNs model, and the learned model is further applied to test images for the detection of unknown buildings. Various experiments using the Datasets I and II (in Section V-A) show that the proposed framework increases the mean average precision values of building detection by 3.63%, 3.85%, and 3.77%, compared with the state-of-the-art methods, i.e., Method IV. Besides, the proposed method is robust to the buildings having different spatial textures and types.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Detection of Asphalt Pavement Potholes and Cracks Based on the Unmanned
           Aerial Vehicle Multispectral Imagery
    • Authors: Yifan Pan;Xianfeng Zhang;Guido Cervone;Liping Yang;
      Pages: 3701 - 3712
      Abstract: Asphalt roads are the basic component of a land transportation system, and the quality of asphalt roads will decrease during the use stage because of the aging and deterioration of the road surface. In the end, some road pavement distresses may appear on the road surface, such as the most common potholes and cracks. In order to improve the efficiency of pavement inspection, currently some new forms of remote sensing data without destructive effect on the pavement are widely used to detect the pavement distresses, such as digital images, light detection and ranging, and radar. Multispectral imagery presenting spatial and spectral features of objects has been widely used in remote sensing application. In our study, the multispectral pavement images acquired by unmanned aerial vehicle (UAV) were used to distinguish between the normal pavement and pavement damages (e.g., cracks and potholes) using machine learning algorithms, such as support vector machine, artificial neural network, and random forest. Comparison of the performance between different data types and models was conducted and is discussed in this study, and indicates that a UAV remote sensing system offers a new tool for monitoring asphalt road pavement condition, which can be used as decision support for road maintenance practice.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Potential Geologic Significances of Hertzsprung Basin Revealed by CE-2
           CELMS Data
    • Authors: Zhiguo Meng;Qingshuai Wang;Huihui Wang;Tianxing Wang;Zhanchuan Cai;
      Pages: 3713 - 3720
      Abstract: The Hertzsprung basin (2 °N, 128 °W) is a relatively well-preserved Nectarian-age basin on the Moon farside with a diameter of 570 km. Low FeO+TiO2 abundance (FTA) and substantial topographic elevation change make the Hertzsprung Basin particularly relevant for evaluating the microwave thermal emission (MTE) mechanism over the lunar surface. In this study, microwave sounder (CELMS) data from the Chang'E-2 satellite are employed to investigate MTE features of the Hertzsprung Basin combined with FTA, surface slope, and rock abundance data. The results are as follows. First, the influence of topography on the CELMS data is essentially negligible, at least in low-latitude regions. Second, the brightness temperature (TB) behavior in the basin floor is similar to that in the nearby highlands, indicating homogeneity of the regolith thermophysical parameters in the highland areas. Third, abnormally low TB is interpreted to represent the existence of large boulders, which brings about two distinct and influential mechanisms on the observed T B. Fourth, a warm interior of the shallow lunar crust is inferred because of the high TB anomaly in the basin floor, Michelson crater, and southeastern region. Finally, the Orientale impact event exerted limited influence on the thermophysical structures of the lunar regolith in the study area.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Multipath Analysis and Exploitation for MIMO Through-the-Wall Imaging
    • Authors: Shisheng Guo;Guolong Cui;Lingjiang Kong;Yilin Song;Xiaobo Yang;
      Pages: 3721 - 3731
      Abstract: This paper considers the multipath modeling and exploitation problems for multiple input multiple output through-the-wall imaging radar. A multipath propagation model referring to two parallel walls is established, and the position distribution characteristics of the multipath ghosts in radar image are analyzed by means of geometric approximation. Therein, we find a feature that the locations of the walls can be obtained using the geometric relationship between the true target and its multipath ghosts. After utilizing constant false alarm rate technique and clustering approach, the coordinates of the multipath ghosts and the concealed target are achieved, and the positions of the two walls are obtained. Finally, simulation and experimental results validate the correctness of the multipath model and the validity of the multipath-exploitation-based wall localization method.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Fusion of Polarimetric Features and Structural Gradient Tensors for VHR
           PolSAR Image Classification
    • Authors: Minh-Tan Pham;
      Pages: 3732 - 3742
      Abstract: This paper proposes a fast texture based supervised classification framework for fully polarimetric synthetic aperture radar (PolSAR) images with very high spatial resolution (VHR). With the development of recent polarimetric radar remote sensing technologies, the acquired images contain not only rich polarimetric characteristics but also high spatial content. Thus, the notion of geometrical structures and heterogeneous textures within VHR PolSAR data becomes more and more significant. Moreover, when the spatial resolution is increased, we need to deal with large-size image data. In this paper, our motivation is to characterize textures by incorporating (fusing) both polarimetric and structural features, and then use them for classification purpose. First, polarimetric features from the weighted coherency matrix and local geometric information based on the Di Zenzo structural tensors are extracted and fused using the covariance approach. Then, supervised classification task is performed by using Riemannian distance measure relevant for covariance-based descriptors. In order to accelerate the computational time, we propose to perform texture description and classification only on characteristic points, not all pixels from the image. Experiments conducted on the VHR F-SAR data as well as the AIRSAR Flevoland image using the proposed framework provide very promising and competitive results in terms of terrain classification and discrimination.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Spaceborne Staring Spotlight SAR Tomography—A First Demonstration
           With TerraSAR-X
    • Authors: Nan Ge;Fernando Rodriguez Gonzalez;Yuanyuan Wang;Yilei Shi;Xiao Xiang Zhu;
      Pages: 3743 - 3756
      Abstract: With the objective of exploiting hardware capabilities and preparing the ground for the next-generation X-band synthetic aperture radar (SAR) missions, TerraSAR-X and TanDEM-X are now able to operate in staring spotlight mode, which is characterized by an increased azimuth resolution of approximately 0.24 m compared with 1.1 m of the conventional sliding spotlight mode. In this paper, we demonstrate for the first time its potential for SAR tomography (TomoSAR). To this end, we tailored our interferometric and tomographic processors for the distinctive features of the staring spotlight mode, which will be analyzed accordingly. By means of its higher spatial resolution, the staring spotlight mode will not only lead to a denser point cloud but also to more accurate height estimates due to the higher signal-to-clutter ratio. As a result of a first comparison between sliding and staring spotlight TomoSAR, first, the density of the staring spotlight point cloud is approximately $text{5.1}$– $text{5.5}$ times as high; and, second, the relative height accuracy of the staring spotlight point cloud is approximately $text{1.7}$ times as high.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Impact of Wind-Induced Scatterers Motion on GB-SAR Imaging
    • Authors: Marc Lort;Albert Aguasca;Carlos López-Martínez;Xavier Fabregas;
      Pages: 3757 - 3768
      Abstract: Ground-based synthetic aperture radar (GB-SAR) sensors represent a cost-effective solution for change detection and ground displacement assessment of small-scale areas in real-time early warning applications. GB-SAR systems based on stepped linear frequency modulated continuous wave signals have led to several improvements such as a significant reduction of the acquisition time. Nevertheless, the acquisition time is still long enough to force a degradation of the quality of the reconstructed images because of possible short-term variable reflectivity of the scenario. This reduction of the quality may degrade the differential interferometric detection process. In scenarios where interesting targets are surrounded by vegetation, this is normally related to atmospheric conditions, in particular, the wind. The present paper characterizes the effect of the short-term variable reflectivity in the GB-SAR image reconstruction and evaluates its equivalent blurring effect, the decorrelation introduced in the SAR images, and the degradation of the extracted parameters. In order to validate the results, the study assesses different GB-SAR images obtained with the RISKSAR-X sensor, which has been developed by the Universitat Politècnica de Catalunya.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • A New Nonlocal Method for Ground-Based Synthetic Aperture Radar
           Deformation Monitoring
    • Authors: Zheng Wang;Zhenhong Li;Jon P. Mills;
      Pages: 3769 - 3781
      Abstract: Ground-based synthetic aperture radar (GBSAR) interferometry offers an effective solution for the monitoring of surface displacements with high precision. However, coherence estimation and phase filtering in GBSAR interferometry is often based on a rectangular window, resulting in estimation biases and resolution loss. To address these issues, conventional nonlocal methods developed for spaceborne synthetic aperture radar are investigated with GBSAR data for the first time. Based on investigation and analysis, an efficient similarity measure is proposed to identify pixels with similar amplitude behaviors and a comprehensive nonlocal method is presented on the basis of this concept with the aim of overcoming current limitations. Pixels with high similarity are identified from a large search window for each point based on a stack of GBSAR single look complex images. Coherence is calculated based on the selected sibling pixels and then enhanced by the second kind statistic estimator. Nonlocal means filtering is also performed based on the sibling pixels to reduce interferometric phase noise. Experiments were conducted using short- and long-term GBSAR interferograms. Qualitative and quantitative analyses of the proposed nonlocal method and other existing techniques demonstrate that the new approach has advantages in terms of coherence estimation and phase filtering capability. The proposed method was integrated into a complete GBSAR small baseline subset algorithm and a time series analysis was achieved for two stacks of data sets. Considered alongside experimental results, this successful application demonstrates the feasibility of the proposed nonlocal method to facilitate the adoption of GBSAR for deformation monitoring applications.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • High-Resolution Three-Dimensional Displacement Retrieval of Mining Areas
           From a Single SAR Amplitude Pair Using the SPIKE Algorithm
    • Authors: Zefa Yang;Zhiwei Li;Jianjun Zhu;Axel Preusse;Jun Hu;Guangcai Feng;Markus Papst;
      Pages: 3782 - 3793
      Abstract: High-resolution three-dimensional (3-D) displacements of mining areas are crucial to assess mining-related geohazards and understand the mining deformation mechanism. In 2018, we proposed a cost-effective and robust method for retrieving mining-induced 3-D displacements from a single SAR amplitude pair (SAP) using offset tracking (OT) procedures. Hereafter, we refer to this method as the “alternative OT-SAP” (AOT-SAP) method. A key step in the AOT-SAP method is solving the 3-D surface displacements from the AOT-SAP-constructed linear system using routine lower-upper (LU) factorization. However, if the AOT-SAP method is used to retrieve high-resolution 3-D displacements, the dimension of the linear system becomes very large (in the order millions), and a high-end supercomputer is often needed to perform the LU-based solving procedure. This significantly narrows the practical application of the AOT-SAP method, considering the limited availability of supercomputers. In this paper, owing to the banded nature of the AOT-SAP-constructed linear system, we introduce the SPIKE algorithm as an alternative to LU factorization to solve high-resolution mining-induced 3-D displacements. The SPIKE algorithm is a divide-and-conquer direct solver of a large banded system, which can parallelly or sequentially solve a large banded linear system, with a much smaller memory requirement and a shorter time cost than LU factorization. This allows us to retrieve the high-resolution 3-D mining-induced displacements with the AOT-SAP method on either a supercomputer or a standard personal computer. Finally, the accuracy of the retrieved 3-D displacements and the efficiency improvement of the SPIKE algorithm were tested using both simulation analysis and a real dataset.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • SAR-Oriented Visual Saliency Model and Directed Acyclic Graph Support
           Vector Metric Based Target Classification
    • Authors: Moussa Amrani;Feng Jiang;Yunzhong Xu;Shaohui Liu;Shengping Zhang;
      Pages: 3794 - 3810
      Abstract: The performance of a synthetic aperture radar automatic (SAR) target recognition system mainly depends on feature extraction and classification. It is crucial to select discriminative features to train a classifier to achieve desired performance. In this paper, we propose an efficient feature extraction and classification algorithm based on a visual saliency model. First, an SAR-oriented graph-based visual saliency model is introduced. Second, relying on the ability of our saliency model in highlighting the most significant regions, Gabor and histogram of oriented gradients features are extracted from the processed SAR images. Third, in order to have more discriminative features, the discrimination correlation analysis algorithm is used for feature fusion and combination. Finally, a two-level directed acyclic graph (DAG) support vector metric learning is developed that seamlessly takes advantage of a two-level DAG by eliminating weak classifiers and the Mahalanobis distance-based radial basis function kernel which emphasizes relevant features and reduces the influence of noninformative features. Experiments on real SAR data from the MSTAR database are conducted and the experimental results demonstrate that the proposed method outperforms the state-of-the-art methods.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • A Hybrid Method for Stability Monitoring in Low-Coherence Urban Regions
           Using Persistent and Distributed Scatterers
    • Authors: Guoqiang Shi;Hui Lin;Peifeng Ma;
      Pages: 3811 - 3821
      Abstract: To perform better monitoring of infrastructure stability in urban and suburban regions, we propose an improved method based on the combined analysis of persistent scatterer (PS) and distributed scatterer (DS). Previous work [13] is extended to explore the DS measurements by exploiting an adaptive homogeneous filter and the capon-based estimator. In this paper, PSs are detected as reference points in the first-tier network. Parameters along network arcs are estimated through the integrated use of beam-forming and an M-estimator. In the second-tier network, we design an adaptive homogeneous filter to cluster statistically homogeneous pixels. DS candidates are then connected to their nearest PS references, establishing the DS network. We estimate DS parameters by using capon beamforming. As the proposed method can provide more complete deformation details of low-coherence targets, it is more effective in stability monitoring. Results from 28 C-band ENVISAT-ASAR scenes of the Hong Kong International Airport are presented in this paper.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Radiometric Cross-Calibration of Landsat-8/OLI and GF-1/PMS Sensors Using
           an Instrumented Sand Site
    • Authors: Yongguang Zhao;Lingling Ma;Chuanrong Li;Caixia Gao;Ning Wang;Lingli Tang;
      Pages: 3822 - 3829
      Abstract: The panchromatic and multispectral (PMS1/PMS2) sensors designed with one panchromatic band and four multispectral bands are two optical cameras on-board the Gao Fen 1 (GF-1) satellite launched on April 26, 2013. The spatial resolution of PMS1/PMS2 are 2 and 8 m of pan and multispectral, respectively. The vicarious calibration of GF-1/PMS was performed by China Centre for Resource Satellite Data and Application, and the calibration coefficients were released once a year. Due to the lack of on-board calibrator, the on-orbit radiometric performance evaluation of the PMS sensors is very important for the further quantitative application of the data. To evaluate the radiometric performance of PMS1 sensor on-board GF-1 satellite, this paper attempts to calibrate PMS1 sensor against the well-calibrated OLI sensor on-board Landsat-8. An instrumented sand site was used, which is located in the national high resolution remote sensing comprehensive calibration site, Baotou, China. The cross-calibration coefficients of PMS1 on October 5, 2015 and August 20, 2016 are calculated and compared with the official calibration coefficients published in 2015 and 2016. The results show that the cross-calibration of PMS sensor using other sensors is necessary, due to the low updating frequency of calibration coefficient for GF-1/PMS sensor. The uncertainty estimation results show that the total uncertainties associated with the cross-calibration of Landsat-8/OLI and GF-1/PMS sensors over a desert site are within 5.5%.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Universal Golomb–Rice Coding Parameter Estimation Using Deep Belief
           Networks for Hyperspectral Image Compression
    • Authors: Zhuocheng Jiang;W. David Pan;Hongda Shen;
      Pages: 3830 - 3840
      Abstract: For efficient compression of hyperspectral images, we propose a universal Golomb-Rice coding parameter estimation method using deep belief network, which does not rely on any assumption on the distribution of the input data. We formulate the problem of selecting the best coding parameter for a given input sequence as a supervised pattern classification problem. Simulations on the synthesized data and five hyperspectral image datasets show that we can achieve significantly more accurate estimation of the coding parameters, which can translate to slightly higher compression than three state-of-the-art methods. More extensive simulations on additional images from the 2006 AVIRIS datasets show that the proposed method achieved overall compression bitrates comparable with other estimation methods, as well as the sample-adaptive entropy coder employed by the Consultative Committee for Space Data Systems standard for multispectral and hyperspectral data compression. Regarding computational feasibility, we show how to use transferable deep belief networks to speed up training by about five times. We also show that inferring the best coding parameters using a trained deep belief network offers computational advantages over the brute-force search method. As an extension, we propose a novel side-information free codec, where the intersequence correlations can be learned by a differently trained network based on the current sequence to predict reasonably good parameters for coding the next sequence. As another extension, we introduce a variable feature combination architecture, where problem specific heuristics such as the sample means can be incorporated to further improve the estimation accuracy.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • An Efficient Real-Time FPGA Implementation of the CCSDS-123 Compression
           Standard for Hyperspectral Images
    • Authors: Johan Fjeldtvedt;Milica Orlandić;Tor Arne Johansen;
      Pages: 3841 - 3852
      Abstract: Hyperspectral imaging (HSI) can extract information from scenes on the earth surface acquired by airborne or spaceborne sensors. On-board processing of HSI is characterized by large datasets on one side and limited processing time and communication links on the other. The CCSDS-123 algorithm is a compression standard assembled for space-related application that allows compacted data transmission via a transmission link. In this paper, a low-complexity high-throughput field-programmable gate array (FPGA) implementation of CCSDS-123 compression algorithm with band interleaved by pixel ordering is presented. Hardware accelerators implemented in FPGAs are increasingly used for custom tasks due to their efficiency, low power, and reconfigurability. The proposed implementation of CCSDS-123 compression standard has been tested on ZedBoard development board containing a Zynq-7020 FPGA. The results are verified against an existing software implementation. The synthesized design can perform on-the-fly processing of hyperspectral images with maximum operating frequency of 147 MHz. The achieved throughput of 147 Msamples/s (2.35 Gb/s) is higher when compared with the throughput reported in recent state-of-the-art FPGA implementations.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Estimation of the Noise Spectral Covariance Matrix in Hyperspectral Images
    • Authors: Asad Mahmood;Amandine Robin;Michael Sears;
      Pages: 3853 - 3862
      Abstract: Accurate estimation of the underlying noise is vital in the processing of hyperspectral images (HSIs). Previous studies have shown that many HSI processing algorithms perform poorly if the noise is not correctly estimated. The classic residual (CR) method is commonly employed for the estimation of variance of the noise in different spectral bands. However, noise estimates as per the CR method ignore the presence of spectral or spatial correlation amongst noise samples. Some studies have been conducted in the past to investigate the spectral and spatial correlation present in the noise but there are very few methods available to estimate the correlations present in the noise. In this paper, we present a reliable method for the estimation of the spectral correlation in the noise. Recently, it was shown that the CR method performs poorly when estimating the spectral correlation in the noise. By using both artificial and real datasets, we show that the proposed new method estimates the noise spectral correlation significantly more accurately than the CR method.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • A Robust PCA Approach With Noise Structure Learning and Spatial–Spectral
           Low-Rank Modeling for Hyperspectral Image Restoration
    • Authors: Wenfei Cao;Kaidong Wang;Guodong Han;Jing Yao;Andrzej Cichocki;
      Pages: 3863 - 3879
      Abstract: Hyperspectral images (HSIs), during the acquisition process, are often corrupted by a mixture of several types of noises, including Gaussian noise, impulsive noise, dead lines, stripes, and many others. These mixed noises not only severely degrade the visual quality of HSIs, but also limit the related subsequent applications. In this paper, we propose a novel robust principal component analysis approach for mixed noise removal by fully identifying the intrinsic structures of the mixed noise and clean HSI. Specifically, for the noise modeling, considering that the mixed noise consists of the dense Gaussian noise and sparse noise, and even the noise densities in different bands are disparate, we introduce a series of Gaussian-Laplace mixture distributions with the band-adaptive scale parameters to estimate the mixed noise. For the image modeling, since there exist rich correlations among the spectral bands and many self-similarities over the image blocks, we initialize a spatial-spectral low-rank characterization of the image. Furthermore, we impose the anisotropic spatial-spectral total variation regularization on the image to enhance the robustness of our approach. Then, by combining the expectation-maximization algorithm and the alternative direction method of multiplier, we develop an efficient algorithm for the resulting optimization problem. Extensive experimental results on the simulated and real datasets demonstrate that the proposed method is superior over the existing state-of-the-art ones.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Deep Multiple Feature Fusion for Hyperspectral Image Classification
    • Authors: Xianghai Cao;Renjie Li;Li Wen;Jie Feng;Licheng Jiao;
      Pages: 3880 - 3891
      Abstract: Hyperspectral images usually have high-dimensional and abundant spectral information for land-cover types classification. Research shows that multiple kinds of features would be helpful to the classification task. In this paper, a new feature fusion framework-deep multiple feature fusion (DMFF)-is proposed for the classification of the hyperspectral image. First, several different features are extracted for each pixel. Then, these features are fed to a deep random forest classifier. With a multiple-layer structure, the outputs of preceding layers will be used as the inputs of the subsequent layers. After the final layer, the classification probability will be computed. By introducing the information of neighboring pixels, the spectral-spatial information is combined effectively. Besides, the structure of the DMFF is easy to expand. Experimental results based on two widely used hyperspectral datasets (Indian pines image and Pavia University image) demonstrate that the proposed method can achieve a satisfactory classification performance compared with other multiple feature fusion methods.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Voxel-Based Extraction of Transmission Lines From Airborne LiDAR Point
           Cloud Data
    • Authors: Juntao Yang;Zhizhong Kang;
      Pages: 3892 - 3904
      Abstract: The safety of the electricity infrastructure significantly affects both our daily life and industrial activities. Timely and accurate monitoring of the safety of electricity network can prevent dangerous situations effectively. Thus, we, in this paper, develop a voxel-based method for automatically extracting the transmission lines from airborne LiDAR point cloud data. The method proposed in this paper uses three-dimensional (3-D) voxels as primitives and consist of the following steps: First, skeleton structure extraction using Laplacian smoothing; second, feature construction of a 3-D voxel using Latent Dirichlet allocation topic model; and third Markov random field model-based extraction for generating locally continuous and globally optimal results. To evaluate the effectiveness and robustness of the proposed method, experiments were conducted on four different types of power line scenes with flat and complex terrains from helicopter-borne LiDAR point cloud data. Experimental results demonstrate that our proposed method is efficient and robust for automatically detecting both the single conductor and the bundled conductors, with precision, recall, and quality of over 96.78%, 98.67%, and 96.66%, respectively. Moreover, compared with other existing methods, our proposed method provides higher detection correctness rate.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • Fusion of Hyperspectral and LiDAR Data Using Discriminant Correlation
           Analysis for Land Cover Classification
    • Authors: Farah Jahan;Jun Zhou;Mohammad Awrangjeb;Yongsheng Gao;
      Pages: 3905 - 3917
      Abstract: It is evident that using complementary features from different sensors is effective for land cover classification. Therefore, combining complementary information from hyperspectral (HS) and light detection and ranging (LiDAR) data can greatly assist in such applications. In this paper, we propose a model for land cover classification, which extracts effective features representing different characteristics (e.g., spectral, geometrical/structural) of objects of interest from these two complementary data sources (e.g., HS and LiDAR) and fuse them effectively by incorporating dimensionality reduction technique. The HS bands are first grouped based on their joint entropy and structural similarity for group-wise spatial feature extraction. The spectral and spatial features from HS are then fused in parallel via discriminant correlation analysis (DCA) method for each band group. This is followed by a multisource fusion step between the spatial features extracted from HS and LiDAR data using DCA. The resultant features from both band-group fusion and multisource fusion steps are concatenated with several other features extracted from HS and LiDAR data. In the proposed model, DCA fusion produces discriminative features by eliminating between-class correlations and confining within-class correlations. We compare the performance of our feature extraction and fusion scheme using random forest and support vector machine classifiers. We also compare our approach with several state-of-the-art approaches on two benchmark land cover datasets and show that our approach outperforms the alternatives by a large margin.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
  • UDWT Domain: A Verified Replacement for Time Domain Implementation of the
           Robust P Phase Picker Algorithm
    • Authors: Mohammad Ali Khajouei;Alireza Goudarzi;
      Pages: 3918 - 3924
      Abstract: Picking the seismic arrival time is an important parameter for the refraction studies. However, random noise, mainly generated by unknown sources, leads to the data quality reduction and incorrect arrival definition. Besides, picking the accurate first arrival time requires expert interpreters. Poor quality of seismic refraction data, related to the high noise amount (because of human being related noise and low energy source) leads to reduce the accuracy of automatic picking methods. In this study, we tried to enhance the accuracy of first break picking by the P phase Picker method using a proper selection of the wavelet type in the discrete wavelet transform domain. UDWT provides and improves the temporal frequency decomposition that can then be followed by thresholding to prepare a denoised signal. To provide the improvement to the P phase picker algorithm, the examples of the synthetic and real data are studied in the time and UDWT domains. Our results demonstrate that the UDWT domain is a reliable replacement for the time-domain implementation of the P phase algorithm.
      PubDate: Oct. 2018
      Issue No: Vol. 11, No. 10 (2018)
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