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Signal Processing Magazine, IEEE

Issue 1 • Date Jan. 2014

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Displaying Results 1 - 24 of 24
  • [Front cover]

    Publication Year: 2014 , Page(s): C1
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  • [Table of contents]

    Publication Year: 2014 , Page(s): 1
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  • Staff Listing

    Publication Year: 2014 , Page(s): 2
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  • Risky Long-Term Research: Two Success Stories in Audio Signal Processing [From the Editor]

    Publication Year: 2014 , Page(s): 4
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  • A Lasting Journey [President's Message]

    Publication Year: 2014 , Page(s): 6
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  • Top Downloads in IEEE Xplore [Reader's Choice]

    Publication Year: 2014 , Page(s): 8 - 9
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  • New Members-at-Large, Directors-at-Large, and Class of Distinguished Lecturers for 2014 [Society News]

    Publication Year: 2014 , Page(s): 10 - 21
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  • Fresh approaches promise wireless quality and reliability improvements [Special Reports]

    Publication Year: 2014 , Page(s): 15 - 18
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  • Automotive Industry Is a Key Component to the Success of the DSP Sector [Special Reports]

    Publication Year: 2014 , Page(s): 18 - 21
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  • Signal and Image Processing in Hyperspectral Remote Sensing [From the Guest Editors]

    Publication Year: 2014 , Page(s): 22 - 23
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  • Detection Algorithms in Hyperspectral Imaging Systems: An Overview of Practical Algorithms

    Publication Year: 2014 , Page(s): 24 - 33
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4074 KB) |  | HTML iconHTML  

    Hyperspectral imaging applications are many and span civil, environmental, and military needs. Typical examples include the detection of specific terrain features and vegetation, mineral, or soil types for resource management; detecting and characterizing materials, surfaces, or paints; the detection of man-made materials in natural backgrounds for the purpose of search and rescue; the detection of specific plant species for the purposes of counter narcotics; and the detection of military vehicles for the purpose of defense and intelligence. The objective of this article is to provide a tutorial overview of detection algorithms used in current hyperspectral imaging systems that operate in the reflective part of the spectrum (0.4 - 24 μm.) The same algorithms might be used in the long-wave infrared spectrum; however, the phenomenology is quite different. The covered topics and the presentation style have been chosen to illustrate the strong couplings among the underlying phenomenology, the theoretical framework for algorithm development and analysis, and the requirements of practical applications. View full abstract»

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  • Hyperspectral Target Detection : An Overview of Current and Future Challenges

    Publication Year: 2014 , Page(s): 34 - 44
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2964 KB) |  | HTML iconHTML  

    Over the last decade, hyperspectral imagery (HSI) obtained by remote sensing systems has provided significant information about the spectral characteristics of the materials in the scene. Typically, a hyperspectral spectrometer provides hundreds of narrow contiguous bands over a wide range of the electromagnetic spectrum. Hyperspectral sensors measure the reflective (or emissive) properties of objects in the visible and short-wave infrared (IR) regions (or the mid-wave and long-wave IR regions) of the spectrum. Processing of these data allows algorithms to detect and identify targets of interest in a hyperspectral scene by exploiting the spectral signatures of the materials [1], [2]. View full abstract»

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  • Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods

    Publication Year: 2014 , Page(s): 45 - 54
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2700 KB) |  | HTML iconHTML  

    The technological evolution of optical sensors over the last few decades has provided remote sensing analysts with rich spatial, spectral, and temporal information. In particular, the increase in spectral resolution of hyperspectral images (HSIs) and infrared sounders opens the doors to new application domains and poses new methodological challenges in data analysis. HSIs allow the characterization of objects of interest (e.g., land-cover classes) with unprecedented accuracy, and keeps inventories up to date. Improvements in spectral resolution have called for advances in signal processing and exploitation algorithms. This article focuses on the challenging problem of hyperspectral image classification, which has recently gained in popularity and attracted the interest of other scientific disciplines such as machine learning, image processing, and computer vision. In the remote sensing community, the term classification is used to denote the process that assigns single pixels to a set of classes, while the term segmentation is used for methods aggregating pixels into objects and then assigned to a class. View full abstract»

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  • Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning

    Publication Year: 2014 , Page(s): 55 - 66
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2173 KB) |  | HTML iconHTML  

    Advances in hyperspectral sensing provide new capability for characterizing spectral signatures in a wide range of physical and biological systems, while inspiring new methods for extracting information from these data. HSI data often lie on sparse, nonlinear manifolds whose geometric and topological structures can be exploited via manifold-learning techniques. In this article, we focused on demonstrating the opportunities provided by manifold learning for classification of remotely sensed data. However, limitations and opportunities remain both for research and applications. Although these methods have been demonstrated to mitigate the impact of physical effects that affect electromagnetic energy traversing the atmosphere and reflecting from a target, nonlinearities are not always exhibited in the data, particularly at lower spatial resolutions, so users should always evaluate the inherent nonlinearity in the data. Manifold learning is data driven, and as such, results are strongly dependent on the characteristics of the data, and one method will not consistently provide the best results. Nonlinear manifold-learning methods require parameter tuning, although experimental results are typically stable over a range of values, and have higher computational overhead than linear methods, which is particularly relevant for large-scale remote sensing data sets. Opportunities for advancing manifold learning also exist for analysis of hyperspectral and multisource remotely sensed data. Manifolds are assumed to be inherently smooth, an assumption that some data sets may violate, and data often contain classes whose spectra are distinctly different, resulting in multiple manifolds or submanifolds that cannot be readily integrated with a single manifold representation. Developing appropriate characterizations that exploit the unique characteristics of these submanifolds for a particular data set is an open research problem for which hierarchical manifold structures appear to h- ve merit. To date, most work in manifold learning has focused on feature extraction from single images, assuming stationarity across the scene. Research is also needed in joint exploitation of global and local embedding methods in dynamic, multitemporal environments and integration with semisupervised and active learning. View full abstract»

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  • A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing

    Publication Year: 2014 , Page(s): 67 - 81
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1529 KB)  

    Blind hyperspectral unmixing (HU), also known as unsupervised HU, is one of the most prominent research topics in signal processing (SP) for hyperspectral remote sensing [1], [2]. Blind HU aims at identifying materials present in a captured scene, as well as their compositions, by using high spectral resolution of hyperspectral images. It is a blind source separation (BSS) problem from a SP viewpoint. Research on this topic started in the 1990s in geoscience and remote sensing [3]-[7], enabled by technological advances in hyperspectral sensing at the time. In recent years, blind HU has attracted much interest from other fields such as SP, machine learning, and optimization, and the subsequent cross-disciplinary research activities have made blind HU a vibrant topic. The resulting impact is not just on remote sensing - blind HU has provided a unique problem scenario that inspired researchers from different fields to devise novel blind SP methods. In fact, one may say that blind HU has established a new branch of BSS approaches not seen in classical BSS studies. In particular, the convex geometry concepts - discovered by early remote sensing researchers through empirical observations [3]-[7] and refined by later research - are elegant and very different from statistical independence-based BSS approaches established in the SP field. Moreover, the latest research on blind HU is rapidly adopting advanced techniques, such as those in sparse SP and optimization. The present development of blind HU seems to be converging to a point where the lines between remote sensing-originated ideas and advanced SP and optimization concepts are no longer clear, and insights from both sides would be used to establish better methods. View full abstract»

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  • Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms

    Publication Year: 2014 , Page(s): 82 - 94
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2075 KB) |  | HTML iconHTML  

    When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM). However, the LMM may be not valid, and other nonlinear models need to be considered, for instance, when there are multiscattering effects or intimate interactions. Consequently, over the last few years, several significant contributions have been proposed to overcome the limitations inherent in the LMM. In this article, we present an overview of recent advances in nonlinear unmixing modeling. View full abstract»

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  • Endmember Variability in Hyperspectral Analysis: Addressing Spectral Variability During Spectral Unmixing

    Publication Year: 2014 , Page(s): 95 - 104
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1958 KB) |  | HTML iconHTML  

    Variable illumination and environmental, atmospheric, and temporal conditions cause the measured spectral signature for a material to vary within hyperspectral imagery. By ignoring these variations, errors are introduced and propagated throughout hyperspectral image analysis. To develop accurate spectral unmixing and endmember estimation methods, a number of approaches that account for spectral variability have been developed. This article motivates and provides a review for methods that account for spectral variability during hyperspectral unmixing and endmember estimation and a discussion on topics for future work in this area. View full abstract»

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  • Compressive Coded Aperture Spectral Imaging: An Introduction

    Publication Year: 2014 , Page(s): 105 - 115
    Cited by:  Papers (2)
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    Imaging spectroscopy involves the sensing of a large amount of spatial information across a multitude of wavelengths. Conventional approaches to hyperspectral sensing scan adjacent zones of the underlying spectral scene and merge the results to construct a spectral data cube. Push broom spectral imaging sensors, for instance, capture a spectral cube with one focal plane array (FPA) measurement per spatial line of the scene [1], [2]. Spectrometers based on optical bandpass filters sequentially scan the scene by tuning the bandpass filters in steps. The disadvantage of these techniques is that they require scanning a number of zones linearly in proportion to the desired spatial and spectral resolution. This article surveys compressive coded aperture spectral imagers, also known as coded aperture snapshot spectral imagers (CASSI) [1], [3], [4], which naturally embody the principles of compressive sensing (CS) [5], [6]. The remarkable advantage of CASSI is that the entire data cube is sensed with just a few FPA measurements and, in some cases, with as little as a single FPA shot. View full abstract»

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  • Sparsity and Structure in Hyperspectral Imaging : Sensing, Reconstruction, and Target Detection

    Publication Year: 2014 , Page(s): 116 - 126
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (914 KB) |  | HTML iconHTML  

    Hyperspectral imaging is a powerful technology for remotely inferring the material properties of the objects in a scene of interest. Hyperspectral images consist of spatial maps of light intensity variation across a large number of spectral bands or wavelengths; alternatively, they can be thought of as a measurement of the spectrum of light transmitted or reflected from each spatial location in a scene. Because chemical elements have unique spectral signatures, observing the spectra at a high spatial and spectral resolution provides information about the material properties of the scene with much more accuracy than is possible with conventional three-color images. As a result, hyperspectral imaging is used in a variety of important applications, including remote sensing, astronomical imaging, and fluorescence microscopy. View full abstract»

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  • Image Inpainting : Overview and Recent Advances

    Publication Year: 2014 , Page(s): 127 - 144
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5869 KB) |  | HTML iconHTML  

    Image inpainting refers to the process of restoring missing or damaged areas in an image. This field of research has been very active over recent years, boosted by numerous applications: restoring images from scratches or text overlays, loss concealment in a context of impaired image transmission, object removal in a context of editing, or disocclusion in image-based rendering (IBR) of viewpoints different from those captured by the cameras. Although earlier work dealing with disocclusion has been published in [1], the term inpainting first appeared in [2] by analogy with a process used in art restoration. View full abstract»

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  • Royalty-Free Video Coding Standards in MPEG [Standards in a Nutshell]

    Publication Year: 2014 , Page(s): 145 - 155
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    On 7 March 2013, the Moving Picture Experts Group Licensing Association (MPEG LA) and Google announced that they have entered into an agreement granting Google a license to techniques, if the patents in MPEG LA might be essential to VP8. Under this agreement, hardware and software companies are free to use the VP8 technology when developing their own products. Considering that it is now common to find patent disputes in headline news, the patent issues related to video coding standards are no exception. In this article, we report on the recent developments in royalty-free codec standardization in MPEG, particularly Internet video coding (IVC), Web video coding (WVC), and video coding for browser, by reviewing the history of royalty-free standards in MPEG and the relationship between standards and patents. View full abstract»

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  • Securing Digital Reputation in Online Social Media [Applications Corner]

    Publication Year: 2014 , Page(s): 149 - 155
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (989 KB) |  | HTML iconHTML  

    As computing and communication systems evolve rapidly and ubiquitously, it has become convenient and almost effortless for individual users to generate, share, and exchange information on online social media. Through online social media, a wide range of digital content, which covers blogging, forums, reviews, social networking, question-answer databases, digital video, mobile phone photography, and wikis, is created by users and has dramatically changed the way people work and interact. However, the simplicity of creating such digital content online has also led to an increase of users? concern about the trustworthiness of such information. To address the issue of trustworthiness, a widely recognized approach is to evaluate the quality of the online information based on feedback from large scale, virtual word-of-mouth networks where individuals share their own opinions and experiences. The aggregated result of such feedback is called digital reputation. Digital reputation has already been widely adopted by current online social media. For example, viewers on YouTube may ?like? or ?dislike? a video clip, buyers on Amazon share their purchasing experiences, travelers evaluate hotels or restaurants on Yelp, and readers can either ?dig? or ?bury? a piece of social news on Reddit. The reputation-based solution is playing an increasingly important role in influencing users? online social interactions. For example, eBay sellers with established reputations can expect about 8% more revenue than new sellers marketing the same goods [1]; the survey in [2] reveals that the services receiving five-star ratings will attract 20% more revenue than the same services receiving four-star ratings. View full abstract»

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  • [Dates Ahead]

    Publication Year: 2014 , Page(s): 156
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  • The Discipline of Signal Processing: Part 2 [Reflections]

    Publication Year: 2014 , Page(s): 157 - 159
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    Discusses the field of signal processing engineering and reports on applications and technologies supported by its use. View full abstract»

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Aims & Scope

IEEE Signal Processing Magazine publishes tutorial-style articles on signal processing research and applications, as well as columns and forums on issues of interest.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Min Wu
University of Maryland, College Park
United States 

http://www/ece.umd.edu/~minwu/