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  Subjects -> ELECTRONICS (Total: 153 journals)
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 1)
Advances in Electronics     Open Access   (Followers: 3)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 5)
Advances in Microelectronic Engineering     Open Access   (Followers: 2)
Advances in Power Electronics     Open Access   (Followers: 7)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 95)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 12)
Annals of Telecommunications     Hybrid Journal   (Followers: 4)
APL : Organic Electronics and Photonics     Hybrid Journal   (Followers: 1)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 6)
Archives of Electrical Engineering     Open Access   (Followers: 9)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 5)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 10)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 16)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 15)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 5)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 5)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 2)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access  
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 13)
China Communications     Full-text available via subscription   (Followers: 4)
Circuits and Systems     Open Access   (Followers: 9)
Consumer Electronics Times     Open Access   (Followers: 4)
Control Systems     Hybrid Journal   (Followers: 26)
Electronic Design     Partially Free  
Electronic Markets     Hybrid Journal   (Followers: 5)
Electronic Materials Letters     Hybrid Journal   (Followers: 3)
Electronics     Open Access   (Followers: 9)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 5)
Electronics Letters     Hybrid Journal   (Followers: 20)
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 23)
Energy Harvesting and Systems : Materials, Mechanisms, Circuits and Storage     Hybrid Journal   (Followers: 1)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 9)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Frequenz     Hybrid Journal   (Followers: 3)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 2)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 20)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 3)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 15)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 13)
IEEE Consumer Electronics Magazine     Full-text available via subscription   (Followers: 17)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 12)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 3)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 7)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 10)
IEEE Transactions on Audio, Speech, and Language Processing     Hybrid Journal   (Followers: 13)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 26)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 17)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 8)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 14)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 21)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 8)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 7)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 6)
IET Power Electronics     Hybrid Journal   (Followers: 14)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 10)
IETE Journal of Education     Open Access   (Followers: 2)
IETE Journal of Research     Open Access   (Followers: 9)
IETE Technical Review     Open Access   (Followers: 4)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 12)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 3)
Informatik-Spektrum     Hybrid Journal  
Instabilities in Silicon Devices     Full-text available via subscription  
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 2)
International Journal of Advanced Electronics and Communication Systems     Open Access   (Followers: 5)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 20)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 3)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 12)
International Journal of Antennas and Propagation     Open Access   (Followers: 7)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 1)
International Journal of Biomedical Nanoscience and Nanotechnology     Hybrid Journal   (Followers: 6)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 4)
International Journal of Computer & Electronics Research     Full-text available via subscription   (Followers: 2)
International Journal of Control     Hybrid Journal   (Followers: 13)
International Journal of Electronics     Hybrid Journal   (Followers: 2)
International Journal of Electronics & Data Communication     Open Access   (Followers: 4)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 3)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 1)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 1)
International Journal of Nano Devices, Sensors and Systems     Open Access   (Followers: 4)
International Journal of Nanoscience     Hybrid Journal  
International Journal of Numerical Modelling:Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 2)
International Journal of Power Electronics     Hybrid Journal   (Followers: 8)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 2)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 3)
International Journal of Superconductivity     Open Access  
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 2)
International Journal on Communication     Full-text available via subscription   (Followers: 9)
International Journal on Electrical and Power Engineering     Full-text available via subscription   (Followers: 10)
International Transaction of Electrical and Computer Engineers System     Open Access  
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 2)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 5)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 1)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription  
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 4)
Journal of Electrical Bioimpedance     Full-text available via subscription   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 2)

        1 2 | Last

Journal Cover   Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
  [SJR: 1.632]   [H-I: 19]   [17 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1939-1404
   Published by Institute of Electrical and Electronics Engineers (IEEE) Homepage  [176 journals]
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote
           Sensing Information for Authors
    • PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • IEEE Transactions on Geoscience and Remote Sensing institutional listings
    • PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote
           Sensing publication information
    • PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Front cover
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Table of Contents
    • Pages: 3253 - 3255
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Letter From the Guest Editors of IGARSS 2014/35th CSRS JSTARS Special
    • Authors: Bernier; M.;Levesque, J.;Garneau, J.;Ledrew, E.;
      Pages: 3256 - 3259
      Abstract: The papers in this special issue were presented at the 35th IGARSS 2014 Canadian Symposium on Remote Sensing event was held from July 13 to 18 in Quebec City, Canada.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Anomaly Detection Using Causal Sliding Windows
    • Authors: Chang; C.;Wang, Y.;Chen, S.;
      Pages: 3260 - 3270
      Abstract: Anomaly detection using sliding windows is not new but using causal sliding windows has not been explored in the past. The need of causality arises from real-time processing where the used sliding windows should not include future data samples that have not been visited, i.e., data samples come in after the currently being processed data sample. This paper develops an approach to anomaly detection using causal sliding windows, which has the capability of being implemented in real time. In doing so, three types of causal windows are defined: 1) causal sliding square matrix windows; 2) causal sliding rectangular matrix windows; and 3) causal sliding array windows. By virtue of causal sliding windows, a causal sample covariance/correlation matrix can be derived for causal anomaly detection. As for the causal sliding array windows, recursive update equations are also derived and thus speed up real-time processing.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Improved SAR Amplitude Image Offset Measurements for Deriving
           Three-Dimensional Coseismic Displacements
    • Authors: Wang; T.;Jonsson, S.;
      Pages: 3271 - 3278
      Abstract: Offsets of synthetic aperture radar (SAR) images have played an important role in deriving complete three-dimensional (3-D) surface displacement fields in geoscientific applications. However, offset maps often suffer from multiple outliers and patch-like artifacts, because the standard offset-measurement method is a regular moving-window operation that does not consider the scattering characteristics of the ground. Here, we show that by focusing the offset measurements on predetected strong reflectors, the reliability and accuracy of SAR offsets can be significantly improved. Application to the 2011 Van (Turkey) earthquake reveals a clear deformation signal from an otherwise decorrelated interferogram, making derivation of the 3-D coseismic displacement field possible. Our proposed method can improve mapping of coseismic deformation and other ground displacements, such as glacier flow and landslide movement when strong reflectors exist.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • An Algorithm for Retrieval of Surface Albedo From Small View-Angle
           Airborne Observations Through the Use of BRDF Archetypes as Prior
    • Authors: Jiao; Z.;Zhang, H.;Dong, Y.;Liu, Q.;Xiao, Q.;Li, X.;
      Pages: 3279 - 3293
      Abstract: Land surface albedo, qualifying the ratio of the radiant flux reflected from the land surface to the incident flux, is a key forcing parameter controlling the Earth’s energy budget. Previously, several BRDF archetypes were distilled from high-quality MODIS BRDF/Albedo products. In this study, we propose a method that largely relies on matching observed multiangular reflectances with the most appropriate of these prior BRDF archetypes to determine the amplitude and shape of the actual surface BRDFs, when directional signatures are insufficient. This method is first evaluated using an assortment of multisource BRDF data sets to demonstrate its viability for surface albedo estimates, and then is applied to airborne wide-angle infrared dual-mode line/area array scanner (WIDAS) from the Watershed Allied Telemetry Experimental Research (WATER) campaign in the Heihe River Basin of China in 2008. This algorithm makes use of the linear MODIS BRDF model to determine the BRDF archetypes needed as prior knowledge for intrinsic spectral albedo estimates. The intrinsic spectral albedos are then used to estimate actual spectral albedos by considering the proportion of direct and diffuse solar radiation. A spectral-to-broadband conversion is performed to generate the broadband albedo at shortwave regimes through the use of conversion coefficients derived from extensive radiative transfer simulations. A further validation confirms that the estimated albedos are consistent with in situ field measured albedos over available corn crop sites. This method provides a major advantage on utilizing generalized BRDF information derived from MODIS in conjunction with other instrument data that are acquired with less angular variation.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Merging Satellite Ocean Color Data With Bayesian Maximum Entropy Method
    • Authors: Shi; Y.;Zhou, X.;Yang, X.;Shi, L.;Ma, S.;
      Pages: 3294 - 3304
      Abstract: Merging multiple satellite ocean color data is one of the ways to create a unified ocean color product and improve the spatial coverage. In this paper, the Bayesian maximum entropy (BME), a probabilistic method, is used to integrate chlorophyll-a (chl-a) concentration data obtained by the seaviewing wide field-of-view sensor (SeaWiFS) on Orbview-2, the medium-resolution imaging spectrometer instrument (MERIS) on ENVISAT and the moderate-resolution imaging spectroradiometer (MODIS) on Aqua. MODIS chl-a concentration on current day is considered as the accurate hard data. A probabilistic model is developed to link hard data and chl-a concentration of other sensors on previous days. The latter are processed as soft data by this probabilistic model to take into account the differences between mission-specific products. The semivariogram of chl-a concentration, which presents the spatial variability and provides a priori knowledge, is developed to improve the spatial coverage. The average daily coverage of the merged chl-a field is 74% for the 1-day temporal integration which is about six times higher than any single mission, and 95% for the 3-day temporal integration which achieves basically a complete global coverage. Root-mean-square error (RMSE) and correlation between in situ chl-a measurements and the BME-merged chl-a from 1-day data are 0.42 and 0.72, and from 3-day data are 0.44 and 0.70, respectively. Compared with the existing GSM method and the weighted averaging (AVW) method, the BME method can greatly improve the spatial coverage and preserve the high accuracy, which demonstrates the potential advantages of the BME method to merge ocean color products from multiple sensors.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Segmentation Fusion for Building Detection Using Domain-Specific
    • Authors: Oztimur Karadag; O.;Senaras, C.;Yarman Vural, F.T.;
      Pages: 3305 - 3315
      Abstract: Segment-based classification is one of the popular approaches for object detection, where the performance of the classification task is sensitive to the accuracy of the output of the initial segmentation. Majority of the object detection systems directly use one of the generic segmentation algorithms, such as mean shift or k-means. However, depending on the problem domain, the properties of the regions such as size, color, texture, and shape, which are suitable for classification, may vary. Besides, fine tuning the segmentation parameters for a set of regions may not provide a globally acceptable solution in remote sensing domain, since the characteristic properties of a class in different regions may change due to the cultural and environmental factors. In this study, we propose a domain-specific segmentation method for building detection, which integrates information related to the building detection problem into the detection system during the segmentation step. Buildings in a remotely sensed image are distinguished from the highly cluttered background, mostly, by their rectangular shapes, roofing material and associated shadows. The proposed method fuses the information extracted from a set of unsupervised segmentation outputs together with this a priori information about the building object, called domain-specific information (DSI), during the segmentation process. Finally, the segmentation output is provided to a two-layer decision fusion algorithm for building detection. The advantage of domain-specific segmentation over the state-of-the-art methods is observed both quantitatively by measuring the segmentation and detection performances and qualitatively by visual inspection.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • SAR Target Recognition via Joint Sparse Representation of Monogenic Signal
    • Authors: Dong; G.;Kuang, G.;Wang, N.;Zhao, L.;Lu, J.;
      Pages: 3316 - 3328
      Abstract: In this paper, the classification via sprepresentation and multitask learning is presented for target recognition in SAR image. To capture the characteristics of SAR image, a multidimensional generalization of the analytic signal, namely the monogenic signal, is employed. The original signal can be then orthogonally decomposed into three components: 1) local amplitude; 2) local phase; and 3) local orientation. Since the components represent the different kinds of information, it is beneficial by jointly considering them in a unifying framework. However, these components are infeasible to be directly utilized due to the high dimension and redundancy. To solve the problem, an intuitive idea is to define an augmented feature vector by concatenating the components. This strategy usually produces some information loss. To cover the shortage, this paper considers three components into different learning tasks, in which some common information can be shared. Specifically, the component-specific feature descriptor for each monogenic component is produced first. Inspired by the recent success of multitask learning, the resulting features are then fed into a joint sparse representation model to exploit the intercorrelation among multiple tasks. The inference is reached in terms of the total reconstruction error accumulated from all tasks. The novelty of this paper includes 1) the development of three component-specific feature descriptors; 2) the introduction of multitask learning into sparse representation model; 3) the numerical implementation of proposed method; and 4) extensive comparative experimental studies on MSTAR SAR dataset, including target recognition under standard operating conditions, as well as extended operating conditions, and the capability of outliers rejection.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Manifold Adaptation for Constant False Alarm Rate Ship Detection in South
           African Oceans
    • Authors: Schwegmann; C.P.;Kleynhans, W.;Salmon, B.P.;
      Pages: 3329 - 3337
      Abstract: The detection of ships at sea is a difficult task made more so by uncooperative ships, especially when using transponder-based ship detection systems. Synthetic aperture radar (SAR) imagery provides a means of observation independent of the ships cooperation, and over the years, a vast amount of research has gone into the detection of ships using this imagery. One of the most common methods used for ship detection in SAR imagery is the cell-averaging constant false alarm rate (CA-CFAR) prescreening method. It uses a scalar threshold value to determine how bright a pixel needs to be in order to be classified as a ship, and thus inversely how many false alarms are permitted. This paper presents by a method of converting the scalar threshold into a threshold manifold. The manifold is adjusted using a simulated annealing (SA) algorithm to optimally fit to information provided by the ship distribution map, which is generated from transponder data. By carefully selecting the input solution and threshold boundaries, much of the computational inefficiencies usually associated with SA can be avoided. The proposed method was tested on six ASAR images against five other methods and had a reported detection accuracy (DA) of 85.2% with a corresponding FAR of math\bf{1.01} \times math\bf{10}^{math\bf{-7}} .
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Change Detection From Differential Airborne LiDAR Using a Weighted
           Anisotropic Iterative Closest Point Algorithm
    • Authors: Zhang; X.;Glennie, C.;Kusari, A.;
      Pages: 3338 - 3346
      Abstract: Differential light detection and ranging (LiDAR) from repeated surveys has recently emerged as an effective tool to measure the three-dimensional (3-D) change. Currently, the primary method for determining 3-D change from LiDAR is through the use of the iterative closest point (ICP) algorithm and its variants, with a simplistic assumption of a uniform accuracy for the entire LiDAR point cloud. This common practice ignores the localization anisotropy and results in local convergence and spurious error estimation. To rigorously determine spatial change, this paper introduces an anisotropic-weighted ICP (A-ICP) algorithm, and proposes to model the random error for every LiDAR observation in the differential point cloud, and use this as a priori weights in the ICP algorithm. The implementation is evaluated by qualitatively and quantitatively comparing the estimation performance on point clouds with synthetic fault ruptures between standard ICP and A-ICP algorithm. As a further enhancement, we also present a moving window technique to improve A-ICP. Practical application of the combined moving window A-ICP technique is evaluated by estimating post-earthquake slip for the 2010 El Mayor-Cucapah Earthquake (EMC) using pre- and post-event LiDAR. Based on the analysis, moving window A-ICP is able to better estimate the synthetic surface ruptures, and provides a smoother estimate of actual displacement for the EMC earthquake.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Framework for Fusion of Ascending and Descending Pass TanDEM-X Raw DEMs
    • Authors: Deo; R.;Rossi, C.;Eineder, M.;Fritz, T.;Rao, Y.S.;
      Pages: 3347 - 3355
      Abstract: A novel method for calculating optimum incidence angle for the TanDEM-X system using any available digital elevation model (DEM) for the given area is proposed in this study. This method includes the plotting of slopes and aspect of the test area in a statistical way and applying mathematical approach using acquisition geometry in ascending and descending pass TanDEM-X data to optimize the incidence angle for obtaining precise DEM. Furthermore, the TanDEM-X raw DEMs in ascending and descending pass over Mumbai, India are combined using a simple weighted fusion algorithm and the quality of fused DEM thus generated is enhanced. The method adopted for fusion is just an experimental study. The problem of optimum weight selection for fusion has been addressed using height error map and a robust layover shadow mask calculated in “Integrated TanDEM-X Processor” (ITP) during TanDEM-X DEM generation. The height error map is calculated from the interferometric coherence with geometrical considerations and the robust layover and shadow map is calculated using TanDEM-X DEM and the corresponding slant range. Results show a significant reduction in the number of invalid pixels after fusion. In the fused DEM, invalids are only 2.14%, while ascending and descending pass DEMs have 5.02% and 6.34%, respectively. Statistical analysis shows a slight improvement in standard deviation of the error in fused DEM by 8% in urban area and about 5% for the whole scene. Only slight improvement in accuracy of fused DEM can be attributed to the coarse resolution of the SRTM-X DEM used as reference.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Accelerated Probabilistic Learning Concept for Mining Heterogeneous Earth
           Observation Images
    • Authors: Alonso; K.;Datcu, M.;
      Pages: 3356 - 3371
      Abstract: We present an accelerated probabilistic learning concept and its prototype implementation for mining heterogeneous Earth observation images, e.g., multispectral images, synthetic aperture radar (SAR) images, image time series, or geographical information systems (GIS) maps. The system prototype combines, at pixel level, the unsupervised clustering results of different features, extracted from heterogeneous satellite images and geographical information resources, with user-defined semantic annotations in order to calculate the posterior probabilities that allow the final probabilistic searches. The system is able to learn different semantic labels based on a newly developed Bayesian networks algorithm and allows different probabilistic retrieval methods of all semantically related images with only a few user interactions. The new algorithm reduces the computational cost, overperforming existing conventional systems, under certain conditions, by several orders of magnitude. The achieved speed-up allows the introduction of new feature models improving the learning capabilities of knowledge-driven image information mining systems and opening them to Big Data environments.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Batch Methods for Resolution Enhancement of TIR Image Sequences
    • Authors: Addesso; P.;Longo, M.;Maltese, A.;Restaino, R.;Vivone, G.;
      Pages: 3372 - 3385
      Abstract: Thermal infrared (TIR) time series are exploited by many methods based on Earth observation (EO), for such applications as agriculture, forest management, and meteorology. However, due to physical limitations, data acquired by a single sensor are often unsatisfactory in terms of spatial or temporal resolution. This issue can be tackled by using remotely sensed data acquired by multiple sensors with complementary features. When nonreal-time functioning or at least near real-time functioning is admitted, the measurements can be profitably fed to a sequential Bayesian algorithm, which allows to account for the correlation embedded in the successive acquisitions. In this work, we focus on applications that allow the batch processing of the whole data sequences acquired in a fixed time interval. In this case, multiple options for improving the final product are offered by the Bayesian framework, based on both sequential and smoothing techniques. We consider several such Bayesian strategies and comparatively assess their performances in practical applications and through real thermal data acquired by the SEVIRI and MODIS sensors, encompassing the presence of multiple disturbance source, e.g., the cloud coverage of the illuminated scene.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • A Compressive-Sensing-Based Approach for the Detection and
           Characterization of Buried Objects
    • Authors: Ambrosanio; M.;Pascazio, V.;
      Pages: 3386 - 3395
      Abstract: The problem of determining and understanding the nature of buried objects by means of nondestructive and noninvasive techniques represents an interesting issue for a great variety of applications. In this framework, the theory of electromagnetic inverse scattering problems can help in such an issue by starting from the measures of the scattered field collected on a surface. What will be presented in this communication is a two-dimensional (2-D) technique based on the so-called Born approximation (BA) combined with a compressive sensing (CS) approach, in order to improve reconstruction capabilities for a proper class of targets. The use of a multiview-multistatic configuration will be employed together with a multifrequency approach to overcome the limited amount of data due to the single-frequency technique. Therefore, after a first numerical analysis of the performance of the considered algorithm, some numerical examples for 2-D aspect-limited configurations will be presented. The scenario is composed of a simplified scene, which consists of two half-spaces, and with the probes located close to the interface between the two media. As proposed in the following, it is easy to observe that the use of CS for this kind of problems may improve reconstruction capabilities, confirming the validity of the presented approach.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Geolocation of Fast-Moving Objects From Satellite-Based Angle-of-Arrival
    • Authors: Hartzell; S.;Burchett, L.;Martin, R.;Taylor, C.;Terzuoli, A.;
      Pages: 3396 - 3403
      Abstract: Recently, satellite-based systems have been introduced that utilize angle-of-arrival (AOA) measurements to geo-locate objects of interest. In the previous work, we considered the application of nonlinear optimization to AoA-based geolocation to these systems. This previous work, however, assumed that all noise sources were independent. In the case of fast-moving objects, however, there is a significant source of error due to the propagation time inherent in satellite-based observation of objects due to the difference between the location of the object when it is observed by a satellite, and the location of the object when it emitted the signal that is being measured. This introduces a systematic error into the system that cannot be resolved by the system proposed by Burchett et al. In this paper, we extend our prior work to account for the time-delay inherent in satellite-based geolocation systems, making this system accurate for fast-movers as well as fixed or slow-moving objects. Results demonstrating significant improvement in geolocation performance both in terms of accuracy and estimated error bounds are presented.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Assessing the Spectral Sensitivity of Martian Terrains to Iron Oxide
           Variations Using the SPLITS Model
    • Authors: Baranoski; G.V.G.;Kimmel, B.W.;Chen, T.F.;Miranda, E.;
      Pages: 3404 - 3413
      Abstract: The mineralogy and environmental history of Mars are being extensively studied through remote sensing observations paired with laboratory and in situ experiments. A significant portion of these experiments is being devoted to the identification and mapping of different iron oxides present in the Martian terrains. Among these compounds, goethite has been an object of great interest since its occurrence can be interpreted as mineralogical evidence of past aqueous activity on those landscapes. Although such experiments can provide valuable information regarding the presence of these minerals, the scope of the resulting observations may be hindered by logistics and cost-related constraints. We believe that predictive computer simulations can be employed to mitigate some of these constraints and contribute to the generation and validation of hypotheses in this area. Accordingly, we propose the use of SPLITS (Spectral Light Transport Model for Sand) in investigations involving the spectral signatures of iron-bearing regions of Mars. In this paper, we initially demonstrate the predictive capabilities of the SPLITS model in this context through qualitative comparisons of modeled results with actual observations and measured data. Using the resulting modeled reflectance curves as our baseline data, we then perform a series of controlled computational experiments to investigate how variations on goethite and hematite content affect the spectral responses of Martian sand-textured soils.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Single-Scan Stem Reconstruction Using Low-Resolution Terrestrial Laser
           Scanner Data
    • Authors: Kelbe; D.;van Aardt, J.;Romanczyk, P.;van Leeuwen, M.;Cawse-Nicholson, K.;
      Pages: 3414 - 3427
      Abstract: Despite the active research, terrestrial laser scanning (TLS) has remained underutilized for forest structure assessment due to reliance of processing algorithms on high-resolution data, which may be costly and time-consuming to collect. Operational inventories, however, necessitate maximizing sample size while minimizing time and cost. The objective of this study was to assess the performance of a novel technique that enables stem reconstruction from low-resolution, single-scan TLS data in an effort to satisfy performance criteria against operational acquisition constraints. Instead of utilizing the curvature of the tree stem, e.g., by circle or cylinder fitting, we take advantage of the sensor-object geometry and reduce the dimensionality of the modeling to a series of one-dimensional (1-D) line fits. This allowed robust recovery of tree stem structure in a range of New England forest types, for tree stems which subtended at least an angular width of 15 mrad—the beam divergence of our system. Assessment was performed by projecting the three-dimensional (3-D) data onto two-dimensional (2-D) images and evaluating the per-point classification accuracies using manually digitized truth maps. Manual forest inventory measurements were also collected for each {\bf{20}} \times {\bf{20}};{\bf{m}} plot and compared to measurements derived automatically. Good retrievals of stem location ( R^2 = 0.99 , {\bf{RMSE}} = {0.16;{\bf{m}}} ) and diameter at breast height (DBH) ( R^2 = {0.80} , {\bf{RMSE}} = {6.0;{\bf{cm}}} ) were achieved. This study demonstrates that low-resolution sensors may be effective in pr- viding data for operational forest inventories constrained by sample size, time, and cost.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • High-Resolution Three-Dimensional Imaging of Space Targets in Micromotion
    • Authors: Bai; X.;Zhou, F.;Bao, Z.;
      Pages: 3428 - 3440
      Abstract: High-resolution three-dimensional (3-D) radar imaging of space targets in micromotion plays a significant role in space target recognition and space situation awareness; thus, it has attracted extensive attention in recent years. Because of the fast rotation, some scattering centers are occluded by others, i.e., the scattering centers cannot be continuously illuminated by radar in the imaging interval, and their radar echoes are discontinuous. In this paper, a nonparametric 3-D imaging method based on scattering center trajectory association is proposed. It deals with target occlusion using the Riemannian manifold optimization and obtains focused imaging of targets in complex micromotion. The effectiveness of the proposed method is validated using simulated data.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • The Feasibility of Ocean Surface Current Measurement Using Pencil-Beam
           Rotating Scatterometer
    • Authors: Bao; Q.;Dong, X.;Zhu, D.;Lang, S.;Xu, X.;
      Pages: 3441 - 3451
      Abstract: Ocean current is highly related to the interaction between ocean and atmosphere. By measuring the speed and direction of the ocean current from space, we can investigate the ocean–atmosphere interaction on a global scale. The ocean–atmosphere interaction helps to maintain the balance that is essential for planet habitability. However, the conventional scatterometer is unable to measure the ocean current vector. To achieve this, a potentially feasible approach is to use a bigger antenna, a higher PRF, and measure the interferometric phase of two successive echoes. This paper derives four decorrelation factors, and provides the phase error model first. Then, an end-to-end simulation model is established, and it is used to analyze the feasibility of ocean surface current measurement from space. Based on the simulation model, the system parameters are optimized. The simulation results show that the current speed standard deviation (Std), which means the measurement accuracy, in along-track and cross-track direction is smaller than 0.1 m/s when the wind speed is larger than 4 m/s. The swath can be used for current vector inversion that is greater than 70% when the wind speed is larger than 7 m/s. Meanwhile, {{\bf K}_{{\bf pc}}} of the modified scatterometer is computed and the results show that {{\bf K}_{{\bf pc}}} is better than the traditional pencil-beam rotating scatterometer when the wind speed is larger than 6 m/s.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • The Global Precipitation Measurement (GPM) Microwave Imager (GMI):
           Instrument Overview and Early On-Orbit Performance
    • Authors: Draper; D.W.;Newell, D.A.;Wentz, F.J.;Krimchansky, S.;Skofronick-Jackson, G.M.;
      Pages: 3452 - 3462
      Abstract: The Global Precipitation Measurement (GPM) mission is an international satellite mission that uses measurements from an advanced radar/radiometer system on a core observatory as reference standards to unify and advance precipitation estimates made by a constellation of research and operational microwave sensors. The GPM core observatory was launched on February 27, 2014 at 18:37 UT in a 65° inclination nonsun-synchronous orbit. GPM focuses on precipitation as a key component of the Earth’s water and energy cycle, and has the capability to provide near-real-time observations for tracking severe weather events, monitoring freshwater resources, and other societal applications. The GPM microwave imager (GMI) on the core observatory provides the direct link to the constellation radiometer sensors, which fly mainly in polar orbits. The GMI sensitivity, accuracy, and stability play a crucial role in unifying the measurements from the GPM constellation of satellites. The instrument has exhibited highly stable operations through the duration of the calibration/validation period. This paper provides an overview of the GMI instrument and a report of early on-orbit commissioning activities. It discusses the on-orbit radiometric sensitivity, absolute calibration accuracy, and stability for each radiometric channel.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Joining a Discrete Radiative Transfer Model and a Kernel Retrieval
           Algorithm for Soil Moisture Estimation From SAR Data
    • Authors: Stamenkovic; J.;Ferrazzoli, P.;Guerriero, L.;Tuia, D.;Thiran, J.;
      Pages: 3463 - 3475
      Abstract: This paper investigates the problem of retrieving soil moisture under crops using Synthetic Aperture Radar (SAR) data. First, we simulated the time series of L-band SAR signals over agricultural fields using a discrete radiative transfer model (RTM). Full growth cycles of winter wheat, maize, and sugar beet fields sampled during the AgriSAR2006 (Agricultural bio/geophysical retrievals from frequent repeat pass SAR and optical imaging) field campaign were considered. A generally good correspondence between the simulated crop backscattering coefficients and those measured by the airborne L-band E-SAR (Experimental-SAR) system was observed with an average root-mean-square error (RMSE) of 2.32 dB. The highest RMSE of 3.63 dB was obtained by the RTM simulations of HV polarized signals in the wheat field, whereas the smallest RMSE of 1.63 dB is achieved in RTM simulations of HV backscattering coefficients in the field of sugar beet. All discrepancies were critically discussed and interpreted. Then, soil moisture was estimated using a nonlinear inversion technique, support vector regression ( \nu -SVR). The model was trained with the backscatter model simulations obtained by the RTM. For all fields considered, the RMSE of the predicted soil moisture was smaller than 5.5% Vol. and the corresponding correlation coefficient ( r ) was equal to or higher than 0.71.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Research on Methods for Narrow-Band Interference Suppression in Synthetic
           Aperture Radar Data
    • Authors: Zhou; F.;Tao, M.;
      Pages: 3476 - 3485
      Abstract: Narrow-band interference (NBI) poses a hindrance to high-quality imaging for synthetic aperture radar (SAR). It is an under-determined single-channel separation problem. In this paper, we addressed the NBI suppression problem by introducing two advanced data-driven nonparametric techniques: 1) the eigen subspace filtering and 2) the independent component analysis (ICA). Both of these two methods utilize the statistical difference between the useful radar echoes and NBI. The interference-contaminated pulse is decomposed into a set of basis signals, from which the bases corresponding to NBI are selected out. Then, the contribution of NBI is excised by filtering out the corresponding interference components. The performances of these advanced methods are compared with the conventional notch filtering method. The experimental results on real datasets show the effectiveness of the proposed methods.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Improving Pixel-Based Change Detection Accuracy Using an Object-Based
           Approach in Multitemporal SAR Flood Images
    • Authors: Lu; J.;Li, J.;Chen, G.;Zhao, L.;Xiong, B.;Kuang, G.;
      Pages: 3486 - 3496
      Abstract: Most of existing change detection methods could be classified into three groups, the traditional pixel-based change detection (PBCD), the object-based change detection (OBCD), and the hybrid change detection (HCD). Nevertheless, both PBCD and OBCD have disadvantages, and classical HCD methods belong to intuitive decision-level fusion schemes of PBCD and OBCD. There is no optimum HCD method as of yet. Analyzing the complementarities of PBCD and OBCD method, we propose a new unsupervised algorithm-level fusion scheme (UAFS-HCD) in this paper to improve the accuracy of PBCD using spatial context information through: 1) getting the preliminary change mask with PBCD at first to estimate some parameters for OBCD; 2) deriving the unchanged area mask to eliminate the areas without changes, reducing error amplification phenomenon of OBCD; and 3) obtaining the final change mask by means of OBCD method. Taking flood detection with multitemporal SAR data as an example, we compared the new scheme with some classical methods, including PBCD, OBCD, and HCD method and supervised manual trial-and-error procedure (MTEP). The experimental results of flood detection showed that the new scheme was efficient and robust, and its accuracy sometimes can even exceed MTEP.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Modifying the Yamaguchi Four-Component Decomposition Scattering Powers
           Using a Stochastic Distance
    • Authors: Bhattacharya; A.;Muhuri, A.;De, S.;Manickam, S.;Frery, A.C.;
      Pages: 3497 - 3506
      Abstract: Model-based decompositions have gained considerable attention after the initial work of Freeman and Durden. This decomposition, which assumes the target to be reflection-symmetric, was later relaxed in the Yamaguchi et al. decomposition with the addition of the helix parameter. Since then, many decomposition have been proposed where either the scattering model was modified to fit the data or the coherency matrix representing the second-order statistics of the full polarimetric data is rotated to fit the scattering model. In this paper, we propose to modify the Yamaguchi four-component decomposition (Y4O) scattering powers using the concept of statistical information theory for matrices. In order to achieve this modification, we propose a method to estimate the polarization orientation angle (OA) from full-polarimetric SAR images using the Hellinger distance. In this method, the OA is estimated by maximizing the Hellinger distance between the unrotated and the rotated math\bf{T}_{33} and the math\bf{T}_{22} components of the coherency matrix math\bf{[T]} . Then, the powers of the Yamaguchi four-component model-based decomposition (Y4O) are modified using the maximum relative stochastic distance between the math\bf{T}_{33} and the math\bf{T}_{22} components of the coherency matrix at the estimated OA. The results show that the overall double-bounce powers over rotated urban areas have significantly improved with the reduction of volume powers. The percentage of pixels with negative powers have also decreased from the Y4O decomposition. The proposed method is both qualitatively and quan- itatively compared with the results obtained from the Y4O and the Y4R decompositions for a Radarsat-2 C-band San-Francisco dataset and an UAVSAR L-band Hayward dataset.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Feasibility of Inter-Comparing Airborne and Spaceborne Observations of
           Radar Backscattering Coefficients
    • Authors: Kim; S.;Jackson, T.J.;Yueh, S.H.;Xu, X.;Hensley, S.;
      Pages: 3507 - 3519
      Abstract: This paper investigates the feasibility of using an airborne synthetic aperture radar (SAR) to validate spaceborne SAR data. This is directed at soil moisture sensing and the recently launched soil moisture active passive (SMAP) satellite. The value of this approach is related to the fact that vicarious targets such as rain forests and oceans calibrate only the extrema of backscattering coefficients ( {\sigma ^0} ) and that the relationship between soil moisture and {\sigma ^0} is nonlinear. Furthermore, corner reflectors are difficult to deploy to calibrate medium resolution (1–3 km) spaceborne sensors such as the one onboard SMAP. A challenge with the approach is the varying incidence angle ( {{\theta }_{text {\inc}}} ) of the airborne sensor versus the constant value used by SMAP. The impact of this on the inter-comparison of airborne and SMAP data is analyzed through simulation and aircraft data analysis. In the absence of the SMAP SAR data, the airborne SAR and scatterometer {\sigma ^0} from the recent field campaign provided the imaging geometry similar to the spaceborne case. The effect of {{\theta }_{text {\inc}}} on the inter-comparison using these two airborne data sets was found to be small if the landcover within the footprint is homogeneous and if {\sigma ^0} (natural unit) changes very little or approximately linearly with {{\theta }_{text {\inc}}} . Over heterogeneous pixels consisting of pasture, grass, forest, and growi- g corn, the simulation shows that the mean and standard deviation of the difference in {\sigma ^0} between the SAR and scatterometer data are smaller than 0.4 and 0.3 dB, respectively. The test results with the airborne data are generally consistent with the simulation results: the mean and standard deviation of the difference are smaller than 0.9 dB for HH, VV, and HV. These magnitudes are comparable with those of the major sources of the difference: the relative calibration errors of the airborne instruments (  !!0.{3};text {dB} ), speckle noise ( \sim!{0}.{35};text {dB} ), effect of {{\theta }_{text {\inc}}} variation within the footprint (  !!0.{4};text {dB} ), and geolocation uncertainty in the airborne scatterometer data (  !!0.{5};text {dB} ). The findings from this study are expected to apply to the inter-comparison of the SMAP and airborne data after considering the details affecting the comparison: imaging geometry, temporal synchronization, spatial collocation, antenna gain, speckle noise, and spatial resolution. When applied, the inter-comparison will provide more confidence in the calibration of SMAP.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • High-Resolution Bistatic ISAR Image Formation for High-Speed and
           Complex-Motion Targets
    • Authors: Zhang; S.;Sun, S.;Zhang, W.;Zong, Z.;Soon Yeo, T.;
      Pages: 3520 - 3531
      Abstract: The existing bistatic inverse synthetic aperture radar (Bi-ISAR) imaging methods usually uses a “stop-and-go” assumption where the target can be considered not in motion (stop condition) during the fast-time and in motion (go condition) during the slow time. However, for the high-speed target, this assumption is violated; furthermore, the conventional compression via Fourier transform is also invalid due to the quadratic phase term induced by the high-speed motion. In this case, a range compression method using the fractional Fourier transform (FrFT) based on minimum entropy criterion is presented to obtain high-resolution one-dimensional (1-D) range profile. Moreover, to achieve azimuth focusing for the complex-motion target, a new method of imaging time selection based on frequency smooth degree (FSD) is proposed. Simulated and real data are provided to verify the effectiveness of the proposed method.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Tracking of Coastal Swell Fields in SAR Images for Sea Depth Retrieval:
           Application to ALOS L-Band Data
    • Authors: Boccia; V.;Renga, A.;Moccia, A.;Zoffoli, S.;
      Pages: 3532 - 3540
      Abstract: Swell propagation in shallow water sets specific relationships between water depth and swell parameters like swell wavelength and period. These relationships allow coastal bathymetry to be retrieved if swell parameters are measured. Synthetic aperture radar (SAR) is able to image swell waves, and spectral analysis of SAR images is a well-known approach for measuring swell parameters. However, owing to nonlinearities in SAR imaging, speckle and noise, spectral analysis can result in significant bathymetric errors. The paper individuates the conditions in which linear imaging is achieved and presents an algorithm able to preserve the accuracy of the calculated bathymetry against image speckle and noise. The proposed approach includes: 1) image resizing and filtering before spectral analysis; 2) limitations to the domain of the spectral analysis; and 3) spatial smoothing of the estimated parameters. The algorithm is tested on L-band ALOS PALSAR data collected over coastal regions in the Gulf of Naples, Italy, showing that swell can be properly tracked from open sea to shoreline. Dense coverage and submetric accuracy is thus achieved.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Measuring Stratospheric H2O With an Airborne Spectrometer:
           Simulation With Realistic Detector Characteristics
    • Authors: Bani Shahabadi; M.;Huang, Y.;Moreau, L.M.;
      Pages: 3541 - 3545
      Abstract: This study examines the ability of a realistic spectral sensor flying at the tropopause level for retrieving stratospheric H2O and temperature. This paper is an extension of an earlier study; the assumptions to best fit the characteristics of the operational sensors have been updated with the noise characteristics of real sensors. Several tests are conducted to examine the effects of changing spectral coverage and noise level on the quality of the retrieval. The results show that the potential advantage of including far infrared (IR) in the sensor’s spectral coverage is hindered by the realistic noise level of the sensors under consideration. Under the current technology, enabling the far IR at the cost of mid-IR accuracy does not help improve H2O retrieval. Nevertheless, it is possible to achieve the retrieval accuracy of 0.5 ppmv for H2O and 1 K for temperature up to 50 hPa using a realistic sensor. The high sensitivity retrieval is advantageous for detecting the small temporal/spatial scale lower stratospheric moistening episodes.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Synergetic Classification of Long-Wave Infrared Hyperspectral and Visible
    • Authors: Lu; X.;Zhang, J.;Li, T.;Zhang, G.;
      Pages: 3546 - 3557
      Abstract: A decision-level-based synergetic classification method has been proposed to conduct land-cover classification for long-wave infrared (LWIR) and high-resolution visible (VIS) images in this paper. The problem of synergic classification is challenging, since we aim for classification map at the spatial resolution of the VIS image under different imaging modes. The proposed method consists of two stages, i.e., stage of classifications for LWIR hyperspectral (HS) and VIS images separately, and stage of decision-level fusion for both classification results. For LWIR HS image, we have proposed a new semisupervised feature extraction method named as semisupervised local discriminant analysis (SLDA) for SVM classification. In parallel, spatial features which have been extracted from the high-resolution VIS image are used to combine with the spectral features for classification. In the second stage, several common decision-level fusion rules have been employed to integrate both classification results. We also present a context-based opinion pools (CBP) strategy to enhance the classification accuracy. Experiments conducted on the dataset of 2014 IEEE GRSS Data Fusion Contest show the advantage of our proposed SLDA method for HS image, and the effect of spatial–spectral features for high-resolution VIS image. Especially, the presented synergic classification strategy for HS and VIS images has higher overall accuracy and better visual effect than those only using single source image and those compared fusion methods in the experiments.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Progressive Band Processing of Anomaly Detection in Hyperspectral Imagery
    • Authors: Chang; C.;Li, Y.;Hobbs, M.C.;Schultz, R.C.;Liu, W.;
      Pages: 3558 - 3571
      Abstract: Anomalies are generally unknown and unexpected and cannot be detected with prior knowledge. Consequently, it is highly desirable to have them detected in an unsupervised manner on a timely basis. One way to do so is to perform anomaly detection while the process of data collection is still ongoing, so that weak anomalies will not be dominated and overwhelmed by subsequent detected strong anomalies. This paper presents an approach to progressive band processing of anomaly detection (PBP-AD) band by band according to band sequential (BSQ) format. In other words, anomaly detection can be carried out band by band progressively without waiting for entire bands completely acquired. This significant advantage allows anomaly detection to be implemented in real time in the sense of progressive band processing with the data processing taking place and data being collected at the same time. This capability also paves a way for anomaly detection in future satellite data communication and transmission where the data can be processed and down linked from satellites band by band simultaneously.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Spatial Wavelet Statistics of SAR Backscatter for Characterizing Degraded
           Forest: A Case Study From Cameroon
    • Authors: De Grandi; E.C.;Mitchard, E.;Woodhouse, I.H.;De Grandi, G.D.;
      Pages: 3572 - 3584
      Abstract: Forest degradation is an important issue in global environmental studies, albeit not yet well defined in quantitative terms. The present work addresses the problem, by starting with the assumption that forest spatial structure can provide an indication of the process of forest degradation, this being reflected in the spatial statistics of synthetic aperture radar (SAR) backscatter observations. The capability of characterizing landcover classes, such as intact and degraded forest (DF), is tested by supervised analysis of ENVISAT ASAR and ALOS PALSAR backscatter spatial statistics, provided by wavelet frames. The test is conducted in a closed semideciduous forest in Cameroon, Central Africa. Results showed that wavelet variance scaling signatures, which are measures of the SAR backscatter two-point statistics in the combined space-scale domain, are able to differentiate landcover classes by capturing their spatial distribution. Discrimination between intact and DF was found to be enabled by functional analysis of the wavelet scaling signatures of C-band ENVISAT ASAR data. Analytic parameters, describing the functional form of the scaling signatures when fitted by a third-degree polynomial, resulted in a statistically significant difference between the signatures of intact and DF. The results with ALOS PALSAR, on the other hand, were not significant. The technique sets the stage for promising developments for tracking forest disturbance, especially with the future availability of C-band data provided by ESA Sentinel-1.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Polarimetric Decompositions of Temperate Wetlands at C-Band
    • Authors: Brisco; B.;Ahern, F.;Hong, S.;Wdowinski, S.;Murnaghan, K.;White, L.;Atwood, D.K.;
      Pages: 3585 - 3594
      Abstract: C-band SAR is well established as a useful sensor for water resources applications. It is commonly accepted that the backscatter from wetlands that consist of many emergent stems over open water (swamps and marshes) is dominated by a double-bounce scattering mechanism. However, recent observations with fully polarimetric data from Radarsat-2 over the extensive wetlands of the Everglades and numerous small wetlands in Ontario appear to be inconsistent with this interpretation of the backscatter physics. In this paper, we use several forms of polarimetric analysis and decomposition. All of these indicate that the backscatter from small marshes and swamps in Ontario is dominated by polarimetric characteristics normally attributed to the odd-bounce mechanism. This anomalous result might be explained as a consequence of changes in the double-bounce reflectance properties of vegetation as a function of the incidence angle. However, detailed electromagnetic backscatter modeling will be needed to provide a more complete and reliable understanding of the details of backscattering from wetlands with emergent vegetation. Additional observational and theoretical work will be required to document and understand the unusual results we report here. If these results are substantiated, the SAR community must re-interpret the generally accepted meanings of the popular decomposition variables, and introduce new terminology to describe them. This would lead to an improved understanding of the backscatter physics and better use of polarimetric SAR for wetland management applications.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • A New Method for Land Cover Characterization and Classification of
           Polarimetric SAR Data Using Polarimetric Signatures
    • Authors: Jafari; M.;Maghsoudi, Y.;Valadan Zoej, M.J.;
      Pages: 3595 - 3607
      Abstract: Conventional methods for analyzing polarimetric synthetic aperture RADAR (PolSAR) data such as scattering matrix show polarimetric information just in a restricted number of polarization bases, whereas backscattering of the targets has information on wide range of polarizations. In order to solve this problem, polarimetric signatures have been investigated to have a better illustration of the target responses. Polarimetric signatures depict more details of physical information from target backscattering in various polarization bases. This paper presents a new method for generating polarimetric signatures for different features in PolSAR data by changing the polarization basis in the covariance matrix. Furthermore, various land cover classes were evaluated using their polarimetric signatures and the pattern recognition matching methods. On the basis of this background, an object-oriented and knowledge-based classification algorithm is proposed. The main idea of this method is to apply polarimetric signatures of various PolSAR features in the land cover classification. A Radarsat-2 image, acquired in leaf-off season of the forest areas, was chosen for this study. The backscattering from different classes, including six land cover classes: 1) red oak (Or); 2) white pine (Pw); 3) black spruce (Sb); 4) urban (Ur); 5) water (Wa); and 6) ground vegetation (GV) was analyzed by the proposed method. The results reported that the polarimetric signatures of PolSAR features introduce new concepts for the various targets which are different from the polarimetric power signatures. Also, the proposed classification was compared with the object-based form of the supervised Wishart classification as the baseline method. The mean accuracy of the proposed method is 6% better than the supervised Wishart classification.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • TanDEM-X Pol-InSAR Inversion for Mangrove Canopy Height Estimation
    • Authors: Lee; S.;Fatoyinbo, T.E.;
      Pages: 3608 - 3618
      Abstract: This paper presents mangrove canopy height estimations using single- and dual-pol TanDEM-X (TDX) data by means of Pol-InSAR techniques. Using the TDX data for forest applications, the penetration capability of X-band into the volume and the polarimetric diversity of interferometric coherence can create false effects that could sometimes lead to biased three-dimensional (3-D) forest parameter estimation. Moreover, in the case of single-pol TDX acquisition (i.e., one independent complex interferometric coherence), it is not possible to perform the inversion without external topographic information due to the underdetermined problem for Pol-InSAR inversion. To solve these problems, the ground phase in the Pol-InSAR model has been estimated directly from TDX interferograms with an assumption that the underlying topography (i.e., water surface level) over mangroves is flat and negligible. With the estimated ground phase that represents water-level elevation in mangroves, the Pol-InSAR inversion from the single- and dual-pol TDX data did not rely on an external DTM data set. The inversion results were validated against airborne lidar measurements in Campeche, Mexico, and in the Zambezi Delta, Mozambique. The single- and dual-pol inversion results showed a successful inversion performance with a high correlation coefficient (0.851–0.919) and with low RMSEs (1.069–1.727 m). The entire inversion performance quality over mangroves at X-band could reach to 10% height estimation accuracy. In addition to Pol-InSAR inversion results, the location of phase center was checked and assessed. The results showed a surprisingly deep location of phase centers from top mangrove canopy (5–7 m, up to 12.36 m) with a similarity at HH and VV polarization.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • DETER-B: The New Amazon Near Real-Time Deforestation Detection System
    • Authors: Diniz; C.G.;Souza, A.A.d.A.;Santos, D.C.;Dias, M.C.;Luz, N.C.d.;Moraes, D.R.V.d.;Maia, J.S.A.;Gomes, A.R.;Narvaes, I.d.S.;Valeriano, D.M.;Maurano, L.E.P.;Adami, M.;
      Pages: 3619 - 3628
      Abstract: The Brazilian Legal Amazon (BLA), the largest global rainforest on earth, contains nearly 30% of the rainforest on earth. Given the regional complexity and dynamics, there are large government investments focused on controlling and preventing deforestation. The National Institute for Space Research (INPE) is currently developing five complementary BLA monitoring systems, among which the near real-time deforestation detection system (DETER) excels. DETER employs MODIS 250 m imagery and almost daily revisit, enabling an early warning system to support surveillance and control of deforestation. The aim of this paper is to present the methodology and results of the DETER based on AWIFS data, called DETER-B. Supported by 56 m images, the new system is effective in detecting deforestation smaller than 25 ha, concentrating 80% of its total detections and 45% of the total mapped area in this range. It also presents higher detection capability in identifying areas between 25 and 100 ha. The area estimation per municipality is statistically equal to those of the official deforestation data (PRODES) and allows the identification of degradation and logging patterns not observed with the traditional DETER system.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Observation and Modeling of X- and Ku-Band Backscatter of Snow-Covered
           Freshwater Lake Ice
    • Authors: Gunn; G.E.;Brogioni, M.;Duguay, C.;Macelloni, G.;Kasurak, A.;King, J.;
      Pages: 3629 - 3642
      Abstract: This study is the first assessment of winter season backscatter ( boldsymbol{\sigma }^\circ ) evolution for snow-covered lake ice observed by X- (9.6 GHz) and Ku-band (17.2 GHz) ground-based scatterometers (UW-SCAT), collected during the Canadian Snow and Ice Experiment in 2010–2011. The boldsymbol{\sigma }{^\circ} evolution of three ice cover scenarios is observed and simulated using a bubbled ice boldsymbol{\sigma }{^\circ } model. The range resolution of UW-SCAT provides separation of interaction at the snow–ice interface (P1), and within the ice volume and ice–water interface (P2). Ice cores extracted at the end of the observation period indicate a P2 boldsymbol{\sigma }{^\circ} increase of approximately 10–12 decibels (dB) (HH & VV) at X- and Ku-band coincident to tubular bubble development. Similarly, complexity of the ice surface (gray ice) results in increased P1 boldsymbol{\sigma }{^\circ} ({\sim}math\bf{7};math\bf{dB}) . P1 observations indicate that Ku-band is sensitive to snowpack overlying lake ice, with boldsymbol{\sigma }{^\circ} exhibiting a 5 (6) dB drop for VV (HH) when 0.235 m snow is removed. X-band is insensitive to changes in overlying snowpack. A bubbled ice boldsymbol{\sigma }{^\circ} model is presented using dense medium-radiative transfer theory under the quasi-crystalline approximation (DMRT-QCA), w- ich treats bubbles as spherical inclusions within an ice volume. Model runs demonstrate the capability of DMRT to produce observed boldsymbol{\sigma }{^\circ} magnitude using snow and ice observations as input. This study improves the understanding of microwave interaction with bubbled ice near the surface or within the volume. Results from this study indicate that further model development involves bubble shape modification within the ice from spherical to tubular.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Inversion-Based Sensitivity Analysis of Snow-Covered Sea Ice
           Electromagnetic Profiles
    • Authors: Firoozy; N.;Komarov, A.S.;Landy, J.;Barber, D.G.;Mojabi, P.;Scharien, R.K.;
      Pages: 3643 - 3655
      Abstract: For the microwave remote sensing of snow-covered sea ice dielectric profiles, the sensitivity of the normalized radar cross-section data with respect to the complex permittivity and thickness values is investigated. Our results show that the data collected closer to the nadir in monostatic setups, and the data collected closer to the specular angle in bistatic setups represent higher sensitivity values. Using both synthetically and experimentally collected data sets, we demonstrate that the inversion of data sets having higher sensitivity values results in enhanced reconstruction accuracy.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Airborne L-Band Radiometer Mapping of the Dome-C Area in Antarctica
    • Authors: Skou; N.;Kristensen, S.S.;Sobjaerg, S.S.;Balling, J.E.;
      Pages: 3656 - 3664
      Abstract: A 350,text{km} \times 350,text{km} area near the Concordia station on the high plateau of Dome-C in Antarctica has been mapped by an airborne L-band radiometer system. The area was expected to display a rather uniform brightness temperature (TB) close to the yearly mean temperature—well suited for calibration checks for spaceborne instruments like SMOS, Aquarius, and SMAP. The measured TBs show unexpected variations like 8-K variation over 240 km on an east–west profile through Concordia, and in certain local cases, a slope of about 0.7 K/km. Comparing the measured TB map with bottom topography reveals a convincing correlation. Simulations show that variations in bedrock topography can indeed modulate the TB appropriately to explain the observed variations. It is concluded that use of the Dome-C area for calibration check of spaceborne radiometers is indeed viable, but with caution—especially when comparing instruments with different spatial resolutions.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Terrestrial Radar Observations of Dynamic Changes in Alpine Snow
    • Authors: Wiesmann; A.;Caduff, R.;Mutzler, C.;
      Pages: 3665 - 3671
      Abstract: Remote sensing of snow with active and passive microwaves on terrestrial, aerial, and satellite platforms has a long tradition. However, the observation of dynamic processes on alpine slopes is difficult due to fixed satellite orbits and consequently given observation geometry and interval and in some cases, also the lack of spatial resolution. Furthermore, the interferometric phase can only be used for displacement measurements if the displacement direction is more or less in the line of sight direction and the observation interval is shorter than the decorrelation time. The use of a terrestrial radar interferometer allows to overcome some of these constraints thanks to the portability of the system, the possibility to make repeat acquisitions in short intervals, and the regional observation capability. In this study, the GPRI (GAMMA portable radar interferometer, [1]) was used that is easily deployable in the field, produces images at meter scale resolution, and allows repeat acquisitions within a minute. Results of two campaigns conducted in the Swiss Alps prove the potential of terrestrial radar to measure rapid and local changes in snow parameters such as changes in the liquid water content and sudden changes in the snowpack due to skiers and avalanches. Using standard interferometric techniques, it was also possible to compute a regional snow displacement map providing information about creeping snow locations, displacement rates, and history.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • A Neural Network-Based Classification for Sea Ice Types on X-Band SAR
    • Authors: Ressel; R.;Frost, A.;Lehner, S.;
      Pages: 3672 - 3680
      Abstract: We examine the performance of an automated sea ice classification algorithm based on TerraSAR-X ScanSAR data. In the first step of our process chain, gray-level co-occurrence matrix(GLCM)-based texture features are extracted from the image. In the second step, these data are fed into an artificial neural network to classify each pixel. Performance of our implementation is examined by utilizing a time series of ScanSAR images in the Western Barents Sea, acquired in spring 2013. The network is trained on the initial image of the time series and then applied to subsequent images. We obtain a reasonable classification accuracy of at least 70% depending on the choice of our ice-type regime, when the incidence angle range of the training data matches that of the classified image. Computational cost of our approach is sufficiently moderate to consider this classification procedure a promising step toward operational, near-realtime ice charting.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Physical Models of Layered Polar Firn Brightness Temperatures From 0.5 to
           2 GHz
    • Authors: Tan; S.;Aksoy, M.;Brogioni, M.;Macelloni, G.;Durand, M.;Jezek, K.C.;Wang, T.;Tsang, L.;Johnson, J.T.;Drinkwater, M.R.;Brucker, L.;
      Pages: 3681 - 3691
      Abstract: We investigate physical effects influencing 0.5–2 GHz brightness temperatures of layered polar firn to support the Ultra Wide Band Software Defined Radiometer (UWBRAD) experiment to be conducted in Greenland and in Antarctica. We find that because ice particle grain sizes are very small compared to the 0.5–2 GHz wavelengths, volume scattering effects are small. Variations in firn density over cm- to m-length scales, however, cause significant effects. Both incoherent and coherent models are used to examine these effects. Incoherent models include a “cloud model” that neglects any reflections internal to the ice sheet, and the DMRT-ML and MEMLS radiative transfer codes that are publicly available. The coherent model is based on the layered medium implementation of the fluctuation dissipation theorem for thermal microwave radiation from a medium having a nonuniform temperature. Density profiles are modeled using a stochastic approach, and model predictions are averaged over a large number of realizations to take into account an averaging over the radiometer footprint. Density profiles are described by combining a smooth average density profile with a spatially correlated random process to model density fluctuations. It is shown that coherent model results after ensemble averaging depend on the correlation lengths of the vertical density fluctuations. If the correlation length is moderate or long compared with the wavelength ( ${sim}0.6mathrm{x} $ longer or greater for Gaussian correlation function without regard for layer thinning due to compaction), coherent and incoherent model results are similar (within ${sim} 1;mathrm{K}$ ). However, when the correlation length is short compared to the wavelength, coherent model results are significantly different from the incoherent model- by several tens of kelvins. For a 10-cm correlation length, the differences are significant between 0.5 and 1.1 GHz, and less for 1.1–2 GHz. Model results are shown to be able to match the v-pol SMOS data closely and predict the h-pol data for small observation angles.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Feasibility Study of C- and L-band SAR Time Series Data in Tracking
           Indonesian Plantation and Natural Forest Cover Changes
    • Authors: Dong; X.;Quegan, S.;Yumiko, U.;Hu, C.;Zeng, T.;
      Pages: 3692 - 3699
      Abstract: Tropical coverage by Envisat ASAR is sparse in space and time and has limited value for monitoring deforestation. The only available dual-polarized multitemporal dataset over Riau province, Indonesia (nine images in a single year), is used to distinguish and monitor tropical plantations and their dynamics and is compared with annual L-band PALSAR data and land cover maps derived from Landsat data. For the ASAR data, both VV and VH are important in discriminating different types of forest cover; whereas, at L-band, most of the relevant information is in the cross-polarized channel. The ASAR VV (but not the VH) backscatter from acacia plantations is strongly affected by whether the underlying soil is peat or nonpeat, which affects the separability of acacia from oil palm. Maximum likelihood classification of the C-band data gave overall accuracies of 86.2% and kappa coefficient of 0.78 by comparison with land cover maps derived from optical data. This was not improved by combining C- and L-band data. Classification of the C-band time series allows the rotation cycle of acacia plantations to be tracked. The available 4-year annual L-band time series shows potential for monitoring these dynamics, but the 1-year time spacing increases the risk of missing changes masked by the rapid growth of acacia.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Rapid Damage Assessment of Rice Crop After Large-Scale Flood in the
           Cambodian Floodplain Using Temporal Spatial Data
    • Authors: Kwak; Y.;Shrestha, B.B.;Yorozuya, A.;Sawano, H.;
      Pages: 3700 - 3709
      Abstract: The objective of this study was to estimate rice crop damage over the entire Cambodia during a large flood event from July to November 2011. An integrated approach was applied to detect and monitor flood areas with flood depth and duration for near real-time rice crop damage estimation in 2011 by using MODIS time-series imagery. The combined data consists of developed MLSWI, EVI from MODIS, new FID from DEM, land use, and simplified empirical damage curves. These data are expected to play an important role in emergency response efforts and rapid risk assessment for high-risk flood areas in the Cambodian floodplain. A rice crop damage map will be generated, showing areas with different damage levels based on flood duration and floodwater depth, including 25% (8 days, below 1.5 m), 50% (8 days, over 1.5 m; 16 days, below 1.5 m), and 100% (16 days, over 1.5 m). The resulting map was validated and shows about 80% consistency with the government census based on field-scale investigation and survey.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Scaling up to National/Regional Urban Extent Mapping Using Landsat Data
    • Authors: Trianni; G.;Lisini, G.;Angiuli, E.;Moreno, E.A.;Dondi, P.;Gaggia, A.;Gamba, P.;
      Pages: 3710 - 3719
      Abstract: This paper describes a methodology to extract a consistent human settlement extent layer using Landsat data and its implementation in the Google Earth Engine platform. The approach allows the extraction of human settlement extents by means of the existing Landsat 5 and 7 data sets, allowing to check their evolution at 30-m spatial resolution. Since human settlements are the main proxy to people geographical distribution and to building locations, this layer may serve as a mean to disaggregate people/building counts at the regional/national level. The approach is tested in several parts of the world against existing ground truth data at the same spatial resolution in Brazil and China, as well as against extents manually extracted from VHR data in three different geographical areas: 1) Brazil; 2) South East China; and 3) Indonesia.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Investigating the Use of Low Frequencies in Urban Areas: Application to
           NLOS Configurations
    • Authors: Thirion-Lefevre; L.;Guinvarch, R.;
      Pages: 3720 - 3729
      Abstract: Remote sensing of urban areas is mainly conducted at high frequencies, to obtain highly resolved images for classification, target detection, or urban areas monitoring for instance. We propose on the contrary to investigate the use of VHF-band for observation, between 120 and 360 MHz. In particular, our concern is to allow target detection, when the object is not in the line of sight (LOS) of the radar, which is typically an issue in urban areas. The benefits of low frequencies are highligted using measurements and simulations over a simple metallic model of two buildings surrounding a street.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Evaluation of Earth Observation Systems for Estimating Environmental
           Determinants of Microbial Contamination in Recreational Waters
    • Authors: Kotchi; S.O.;Brazeau, S.;Turgeon, P.;Pelcat, Y.;Legare, J.;Lavigne, M.;Essono, F.N.;Fournier, R.A.;Michel, P.;
      Pages: 3730 - 3741
      Abstract: Public health risks related to the microbial contamination of recreational waters are increased by global environmental change. Intensification of agriculture, urban sprawl, and climate change are some of the changes which can lead to favorable conditions for the emergence of waterborne diseases. Earth observation (EO) images have several advantages for the characterization and monitoring of environmental determinants that could be associated with the risk of microbial contamination of recreational waters in vast territories like Canada. There are a large number of EO systems characterized by different spatial, temporal, spectral, and radiometric resolutions. Also, they have different levels of accessibility. In this study, we compared several EO systems for the estimation of environmental determinants to assess their usefulness and their added value in monitoring programs of recreational waters. Satellite images from EO systems WorldView-2, GeoEye-1, SPOT-5/HRG, Landsat-5/TM, Envisat/MERIS, Terra/MODIS, NOAA/AVHRR, and Radarsat-2 were acquired in 2010 and 2011 in southern Quebec, Canada. A supervised classification of these images with a maximum likelihood algorithm was used to estimate five key environmental determinants (agricultural land, impervious surfaces, water, forest, and wetlands) within the area of influence of 78 beaches. Logistic regression models were developed to establish the relationship between fecal contamination of beaches and environmental determinants derived from satellite images. The power prediction of these models and criteria such as accuracy of classified images, the ability of the sensor to detect environmental determinants in the area of influence of beaches, the correlation between the estimated environmental determinants in the area of influence by the sensor with those estimated by very high spatial resolution reference sensors (WorldView-2 and GeoEye-1), and general criteria of accessibility (cost of the images, imaging swath, sat- llite revisit interval, hours of work, and expertise and material required to process the images) were used to evaluate the EO systems. The logistic regression model establishing the relationship between environmental determinants from Landsat-5/TM images and the level of fecal contamination of beaches is the one which performs best. These images are also those that best meet all of the evaluation criteria. This study showed that environmental determinants like agricultural lands and impervious surfaces present in the area of influence of beaches are those which contribute the most to the microbial contamination of beaches. Our study demonstrated the utility and the added value that EO images could bring to programs monitoring the microbial contamination of recreational waters.
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Open Access
    • Pages: 3742 - 3742
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • IEEE xplore digital library
    • Pages: 3743 - 3743
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
  • Expand your professional network with IEEE
    • Pages: 3744 - 3744
      PubDate: July 2015
      Issue No: Vol. 8, No. 7 (2015)
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