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Proceedings of the IEEE

Issue 3 • Date March 2013

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Displaying Results 1 - 24 of 24
  • Front cover

    Page(s): C1
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  • Proceedings of the IEEE publication information

    Page(s): C2
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  • Table of Contents

    Page(s): 557 - 558
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  • Electromagnetic Tomography for Medical and Industrial Applications: Challenges and Opportunities [Point of View]

    Page(s): 559 - 565
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  • Advances in Very-High-Resolution Remote Sensing [Scanning the Issue]

    Page(s): 566 - 569
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  • Human Settlements: A Global Challenge for EO Data Processing and Interpretation

    Page(s): 570 - 581
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (702 KB) |  | HTML iconHTML  

    The availability of fine spatial resolution earth observation (EO) data has been always considered as a plus for human settlement monitoring from space. It calls, however, for new and efficient data processing tools, capable to manage huge data amounts and provide information for urban area monitoring, management, and protection. Issues related to data processing at the global level imply multiple-scale processing and a focused data interpretation approach, starting from human settlement delineation using spatial information and detailing down to material identification and object characterization using multispectral and/or hyperspectral data sets. This paper attempts to provide a consistent framework for information processing in urban remote sensing, stressing the need for a global approach able to exploit the detailed information available from EO data sets. View full abstract»

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  • Using High-Resolution Airborne and Satellite Imagery to Assess Crop Growth and Yield Variability for Precision Agriculture

    Page(s): 582 - 592
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1085 KB) |  | HTML iconHTML  

    With increased use of precision agriculture techniques, information concerning within-field crop yield variability is becoming increasingly important for effective crop management. Despite the commercial availability of yield monitors, many crop harvesters are not equipped with them. Moreover, yield monitor data can only be collected at harvest and used for after-season management. On the other hand, remote sensing imagery obtained during the growing season can be used to generate yield maps for both within-season and after-season management. This paper gives an overview on the use of airborne multispectral and hyperspectral imagery and high-resolution satellite imagery for assessing crop growth and yield variability. The methodologies for image acquisition and processing and for the integration and analysis of image and yield data are discussed. Five application examples are provided to illustrate how airborne multispectral and hyperspectral imagery and high-resolution satellite imagery have been used for mapping crop yield variability. Image processing techniques including vegetation indices, unsupervised classification, correlation and regression analysis, principal component analysis, and supervised and unsupervised linear spectral unmixing are used in these examples. Some of the advantages and limitations on the use of different types of remote sensing imagery and analysis techniques for yield mapping are also discussed. View full abstract»

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  • Active Learning: Any Value for Classification of Remotely Sensed Data?

    Page(s): 593 - 608
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2582 KB) |  | HTML iconHTML  

    Active learning, which has a strong impact on processing data prior to the classification phase, is an active research area within the machine learning community, and is now being extended for remote sensing applications. To be effective, classification must rely on the most informative pixels, while the training set should be as compact as possible. Active learning heuristics provide capability to select unlabeled data that are the “most informative” and to obtain the respective labels, contributing to both goals. Characteristics of remotely sensed image data provide both challenges and opportunities to exploit the potential advantages of active learning. We present an overview of active learning methods, then review the latest techniques proposed to cope with the problem of interactive sampling of training pixels for classification of remotely sensed data with support vector machines (SVMs). We discuss remote sensing specific approaches dealing with multisource and spatially and time-varying data, and provide examples for high-dimensional hyperspectral imagery. View full abstract»

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  • A Novel Framework for the Design of Change-Detection Systems for Very-High-Resolution Remote Sensing Images

    Page(s): 609 - 630
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1631 KB) |  | HTML iconHTML  

    This paper addresses change detection in multitemporal remote sensing images. After a review of the main techniques developed in remote sensing for the analysis of multitemporal data, the attention is focused on the challenging problem of change detection in very-high-resolution (VHR) multispectral images. In this context, we propose a framework that aims at defining a top-down approach to the design of the architecture of novel change-detection systems for multitemporal VHR images. The proposed framework explicitly models the presence of different radiometric changes on the basis of the properties of multitemporal images, extracts the semantic meaning of radiometric changes, identifies changes of interest with strategies designed on the basis of the specific application, and takes advantage of the intrinsic multiscale/multilevel properties of the objects and the high spatial correlation between pixels in a neighborhood. This framework defines guidelines for the development of a new generation of change-detection methods that can properly analyze multitemporal VHR images taking into account the intrinsic complexity associated with these data. In order to illustrate the use of the proposed framework, a real change-detection problem has been considered, which is described by a pair of VHR multispectral images acquired by the QuickBird satellite on the city of Trento, Italy. The proposed framework has been used for defining a system for change detection in the two images. Experimental results confirm the effectiveness of the developed system and the usefulness of the proposed framework. View full abstract»

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  • Land-Cover Mapping by Markov Modeling of Spatial–Contextual Information in Very-High-Resolution Remote Sensing Images

    Page(s): 631 - 651
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3429 KB) |  | HTML iconHTML  

    Markov models represent a wide and general family of stochastic models for the temporal and spatial dependence properties associated to 1-D and multidimensional random sequences or random fields. Their applications range over a wide variety of subareas of the information and communication technology (ICT) field, including networking, automation, speech processing, genomic-sequence analysis, or image processing. Focusing on the applicative problem of land-cover mapping from very-high-resolution (VHR) remote sensing images, which is a relevant problem in many applications of environmental monitoring and natural resource exploitation, Markov models convey a great potential, thanks to their capability to effectively describe and incorporate the spatial information associated with image data into an image-classification process. In this framework, the main ideas and previous work about Markov modeling for VHR image classification will be recalled in this paper and processing results obtained through recent methods proposed by the authors will be discussed. View full abstract»

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  • Advances in Spectral-Spatial Classification of Hyperspectral Images

    Page(s): 652 - 675
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4393 KB) |  | HTML iconHTML  

    Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods. View full abstract»

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  • Feature Mining for Hyperspectral Image Classification

    Page(s): 676 - 697
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3712 KB) |  | HTML iconHTML  

    Hyperspectral sensors record the reflectance from the Earth's surface over the full range of solar wavelengths with high spectral resolution. The resulting high-dimensional data contain rich information for a wide range of applications. However, for a specific application, not all the measurements are important and useful. The original feature space may not be the most effective space for representing the data. Feature mining, which includes feature generation, feature selection (FS), and feature extraction (FE), is a critical task for hyperspectral data classification. Significant research effort has focused on this issue since hyperspectral data became available in the late 1980s. The feature mining techniques which have been developed include supervised and unsupervised, parametric and nonparametric, linear and nonlinear methods, which all seek to identify the informative subspace. This paper provides an overview of both conventional and advanced feature reduction methods, with details on a few techniques that are commonly used for analysis of hyperspectral data. A general form that represents several linear and nonlinear FE methods is also presented. Experiments using two widely available hyperspectral data sets are included to illustrate selected FS and FE methods. View full abstract»

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  • The Promise of Reconfigurable Computing for Hyperspectral Imaging Onboard Systems: A Review and Trends

    Page(s): 698 - 722
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3380 KB) |  | HTML iconHTML  

    Hyperspectral imaging is an important technique in remote sensing which is characterized by high spectral resolutions. With the advent of new hyperspectral remote sensing missions and their increased temporal resolutions, the availability and dimensionality of hyperspectral data is continuously increasing. This demands fast processing solutions that can be used to compress and/or interpret hyperspectral data onboard spacecraft imaging platforms in order to reduce downlink connection requirements and perform a more efficient exploitation of hyperspectral data sets in various applications. Over the last few years, reconfigurable hardware solutions such as field-programmable gate arrays (FPGAs) have been consolidated as the standard choice for onboard remote sensing processing due to their smaller size, weight, and power consumption when compared with other high-performance computing systems, as well as to the availability of more FPGAs with increased tolerance to ionizing radiation in space. Although there have been many literature sources on the use of FPGAs in remote sensing in general and in hyperspectral remote sensing in particular, there is no specific reference discussing the state-of-the-art and future trends of applying this flexible and dynamic technology to such missions. In this work, a necessary first step in this direction is taken by providing an extensive review and discussion of the (current and future) capabilities of reconfigurable hardware and FPGAs in the context of hyperspectral remote sensing missions. The review covers both technological aspects of FPGA hardware and implementation issues, providing two specific case studies in which FPGAs are successfully used to improve the compression and interpretation (through spectral unmixing concepts) of remotely sensed hyperspectral data. Based on the two considered case studies, we also highlight the major challenges to be addressed in the near future in this emerging and fast growing research area. View full abstract»

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  • Processing Multidimensional SAR and Hyperspectral Images With Binary Partition Tree

    Page(s): 723 - 747
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4564 KB) |  | HTML iconHTML  

    The current increase of spatial as well as spectral resolutions of modern remote sensing sensors represents a real opportunity for many practical applications but also generates important challenges in terms of image processing. In particular, the spatial correlation between pixels and/or the spectral correlation between spectral bands of a given pixel cannot be ignored. The traditional pixel-based representation of images does not facilitate the handling of these correlations. In this paper, we discuss the interest of a particular hierarchical region-based representation of images based on binary partition tree (BPT). This representation approach is very flexible as it can be applied to any type of image. Here both optical and radar images will be discussed. Moreover, once the image representation is computed, it can be used for many different applications. Filtering, segmentation, and classification will be detailed in this paper. In all cases, the interest of the BPT representation over the classical pixel-based representation will be highlighted. View full abstract»

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  • Melt Pond Mapping With High-Resolution SAR: The First View

    Page(s): 748 - 758
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (2081 KB) |  | HTML iconHTML  

    Melt pond statistics (size and shape) have previously been retrieved from aerial photography and high-resolution visible satellite data. These submeter- or meter-resolution visible data can provide reasonably accurate information on melt ponds, but are greatly constrained by the limited solar illumination and frequent cloud cover in the Arctic region. In this study, we venture into exploring high-resolution synthetic aperture radar (SAR) or imaging radar method for melt pond mapping, which is not severely disrupted by cloud or low solar zenith angle. We analyzed high-resolution airborne SAR images (0.3-m resolution) of midsummer sea ice, acquired from a helicopter-borne SAR system in the northern Chukchi Sea. The pond area and shape (circularity) derived from the airborne SAR images showed that the statistics were comparable to those previously observed from aerial photographs. We argue that high-resolution SAR, together with one-to-one comparison with coincident aerial photographs, can be used to map melt ponds at a level of detail comparable to aerial photography or high-resolution optical satellite remote sensing. Our encouraging results suggest the possibility of using high-resolution SAR (current or future systems) to map melt ponds in the Arctic region. View full abstract»

    Open Access
  • Very-High-Resolution Airborne Synthetic Aperture Radar Imaging: Signal Processing and Applications

    Page(s): 759 - 783
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4315 KB) |  | HTML iconHTML  

    During the last decade, synthetic aperture radar (SAR) became an indispensable source of information in Earth observation. This has been possible mainly due to the current trend toward higher spatial resolution and novel imaging modes. A major driver for this development has been and still is the airborne SAR technology, which is usually ahead of the capabilities of spaceborne sensors by several years. Today's airborne sensors are capable of delivering high-quality SAR data with decimeter resolution and allow the development of novel approaches in data analysis and information extraction from SAR. In this paper, a review about the abilities and needs of today's very high-resolution airborne SAR sensors is given, based on and summarizing the longtime experience of the German Aerospace Center (DLR) with airborne SAR technology and its applications. A description of the specific requirements of high-resolution airborne data processing is presented, followed by an extensive overview of emerging applications of high-resolution SAR. In many cases, information extraction from high-resolution airborne SAR imagery has achieved a mature level, turning SAR technology more and more into an operational tool. Such abilities, which are today mostly limited to airborne SAR, might become typical in the next generation of spaceborne SAR missions. View full abstract»

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  • Airborne SAR-Efficient Signal Processing for Very High Resolution

    Page(s): 784 - 797
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1268 KB) |  | HTML iconHTML  

    Frequency-domain synthetic aperture radar (SAR) image formation algorithms are of lower computation cost (both in number of elementary operations and in required memory storage) than direct time-domain integration, and do not make the narrowband (monochromatic) assumption. Both advantages are critical to very-high-resolution imaging because a lower complexity yields a drastic computation time decrease as cross-range resolution increases, and the narrowband assumption is more and more a concern as range resolution (hence bandwidth) increases. Though an exact formulation exists (ω- k algorithm) for a perfect linear uniform acquisition trajectory, in a real-life airborne case, the unavoidable trajectory deviation from a straight line needs to be compensated. This motion compensation (MoComp) operation is much more complicated in the case of frequency-domain processing. An efficient technique for this purpose is presented. This method keeps the parallel processing aspect, and has been programmed both for multithread on multicore/symmetrical multiprocessor central processing units (CPUs) and for graphic processor units (GPUs). View full abstract»

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  • Point Target Classification via Fast Lossless and Sufficient \Omega \Psi \Phi Invariant Decomposition of High-Resolution and Fully Polarimetric SAR/ISAR Data

    Page(s): 798 - 830
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    The classification of high-resolution and fully polarimetric SAR/ISAR data has gained a lot of attention in remote sensing and surveillance problems and is addressed by decomposing the radar target Sinclair matrix. In this paper, the Sinclair matrix has been projected onto the circular polarization basis and is decomposed into five parameters that are invariant to the relative phase Φ, the Faraday rotation Ω, and the target orientation Ψ without any information loss. The physical interpretation of these parameters, useful for target classification studies, is found in the wave-particle nature of radar scattering phenomenon given the circular polarization of elemental packets of energy. The proposed deterministic target decomposition is based on the left-orthogonal special unitary SU(2) basis, decomposing the signal backscattered by point targets, represented by the target vector, via six special unitary SU(4) rotation matrices, and by providing full resolution and lossless analysis. Comparisons between the proposed deterministic target decomposition and the Cameron, Kennaugh, Krogager, and Touzi decompositions are also pointed out. Generally, the proposed decomposition provides simpler interpretation, faster parameter extraction, and better generalization properties for the analysis of nonreciprocal or random targets. Several polarimetric SAR/ISAR data sets of UWB data, airborne fully polarimetric EMISAR data, and spaceborne RADARSAT2 are used for illustrating the effectiveness and the usefulness of this decomposition for the classification of point targets. Results are very promising for application use in the next generation of high-resolution spaceborne and airborne Pol-SAR and Pol-ISAR systems. View full abstract»

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  • Monitoring of the March 11, 2011, Off-Tohoku 9.0 Earthquake With Super-Tsunami Disaster by Implementing Fully Polarimetric High-Resolution POLSAR Techniques

    Page(s): 831 - 846
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4081 KB) |  | HTML iconHTML  

    This paper reflects the polarimetric synthetic aperture radar (POLSAR) data utilization for near-real-time earthquake and/or tsunami damage assessment in urban areas. In order to show the potential of the fully polarimetric high-resolution polarimetric SAR (POLSAR) image data sets, a four-component scattering power decomposition scheme has been developed and applied to monitor near-real-time earthquake and tsunami disaster damages. The test site for natural disaster damages has been selected: parts of the coastal area affected by the March 11, 2011, 9.0 magnitude earthquake that struck off Japan's northeastern coast and triggered a super-tsunami. The color-coded images of the scattering power decomposition scheme are a simple and straightforward tool to interpret the changes over the earthquake/tsunami affected urban areas and man-made infrastructures. This method also holds other types of natural (typhoon or tornado) and man-made disaster assessment applications. It is found that the double-bounce scattering power is the most promising of the input parameters to detect automated disaster affected urban areas at pixel level. It is also observed that the very-high-resolution POLSAR images are required for superior urban area monitoring over the oriented urban blocks with respect to the illumination of radar. View full abstract»

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  • Differential analyzers [Scanning Our Past]

    Page(s): 847 - 852
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  • Future special issues/special sections of the Proceedings

    Page(s): 853 - 854
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  • 2013 IEEE membership application

    Page(s): 855 - 856
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  • IEEE Global History Network

    Page(s): C3
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  • [Back cover]

    Page(s): C4
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H. Joel Trussell
North Carolina State University