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6th workshop on Hyperspectral image and signal processing - Evolution in Remote Sensing (WHISPERS)

作者： Marinoni, Andrea Plaza, Javier Plaza, Antonio Gamba, Paolo Univ Pavia Dipartimento Ingn Ind & Informaz Pavia Italy Univ Extremadura Hyperspectral Comp Lab Caceres Spain

ISBN: (纸本)9781467390125

Linear spectral unmixing has been widely used for hyperspectral data interpretation. However, there is a need for nonlinear unmixing methods that can model more complex geometries without the need to resort to prior knowledge about the objects in the scene. In this paper, we present a novel strategy for nonlinear spectral unmixing which combines polytope decomposition (POD) with artificial neural network (ANN)-based learning. Even if no ground-truth information is available, the ANN can still efficiently estimate the order of the nonlinearity involved in the problem and enhance the capacity of the POD method to deliver unmixing performance for a wider range of nonlinearities. the proposed method has been evaluated using both simulated and real scenes, providing promising results.

关键词： nonlinear hyperspectral unmixing artificial neural networks (ANNs) polytope decomposition (POD)

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nonlinear transformation of differential equations into phase space

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EURASIP JOURNAL ON APPLIED signal processing 2004年第12期2004卷 1770-1777页

作者： Cohen, L Galleani, L CUNY Hunter Coll Dept Phys & Astron New York NY 10021 USA Politecn Torino Dipartimento Elettron I-10129 Turin Italy

Time-frequency representations transform a one-dimensional function into a two-dimensional function in the phase-space of time and frequency. the transformation to accomplish is a nonlinear transformation and there are an infinite number of such transformations. We obtain the governing differential equation for any two-dimensional bilinear phase-space function for the case when the governing equation for the time function is an ordinary differential equation with constant coefficients. this connects the dynamical features of the problem directly to the phase-space function and it has a number of advantages.

关键词： time-frequency distributions nonstationary signals linear systems differential equations

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Design of a low-power VLSI macrocell for nonlinear adaptive video noise reduction

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EURASIP JOURNAL ON APPLIED signal processing 2004年第12期2004卷 1921-1930页

作者： Saponara, S Fanucci, L Terreni, P Univ Pisa Dept Informat Engn I-56122 Pisa Italy CNR Inst Elect Informat Engn & Telecommun I-56122 Pisa Italy

A VLSI macrocell for edge-preserving video noise reduction is proposed in the paper. It is based on a nonlinear rational filter enhanced by a noise estimator for blind and dynamic adaptation of the filtering parameters to the input signal statistics. the VLSI filter features a modular architecture allowing the extension of both mask size and filtering directions. Both spatial and spatiotemporal algorithms are supported. Simulation results with monochrome test videos prove its efficiency for many noise distributions with PSNR improvements up to 3.8 dB with respect to a nonadaptive solution. the VLSI macrocell has been realized in a 0.18 mum CMOS technology using a standard-cells library;it allows for real-time processing of main video formats, up to 30 fps (frames per second) 4CIF, with a power consumption in the order of few mW.

关键词： nonlinear image processing video noise reduction adaptive filters very large scale integration architectures low-power design

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nonlinear PARSIMONIOUS FEATURE SELECTION FOR thE CLASSIFICATION OF HYPERSPECTRAL imageS 6

NONLINEAR PARSIMONIOUS FEATURE SELECTION FOR THE CLASSIFICAT...

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6th workshop on Hyperspectral image and signal processing - Evolution in Remote Sensing (WHISPERS)

作者： Fauvel, M. Zullo, A. Ferraty, F. INRA UMR 1201 Lab DYNAFOR Toulouse France INP Toulouse Toulouse France Inst Math Toulouse UMR 5219 Toulouse France Univ Toulouse Toulouse France

ISBN: (纸本)9781467390125

A nonlinear parsimonious feature selection algorithm is presented in this paper. It is based on a Gaussian mixture model (GMM). GMM are used for classifying hyperspectral images. the algorithm selects iteratively spectral features that maximizes an estimate of the correct classification rate. In order to perform fast in terms of computing time, an efficient implementation is proposed. First, the GMM can be updated when the estimation of the correct classification rate is computed, rather re-estimate the full model. Secondly, using marginalization of the GMM, sub models can be directly obtain from the full model learns with all the spectral features. Experimental results for three hyperspectral data sets show that the method performs very well and is able to extract very few spectral channels.

关键词： Hyperspectral image classification nonlinear feature selection Gaussian mixture model parsimony

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DIMENSIONALITY REDUCTION VIA REGRESSION ON HYPERSPECTRAL INFRARED SOUNDING DATA 6

DIMENSIONALITY REDUCTION VIA REGRESSION ON HYPERSPECTRAL INF...

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6th workshop on Hyperspectral image and signal processing - Evolution in Remote Sensing (WHISPERS)

作者： Laparra, Valero Malo, Jesus Camps-Valls, Gustavo Univ Valencia IPL Valencia Spain

ISBN: (纸本)9781467390125

this paper introduces a new method for dimensionality reduction via regression (DRR). the method generalizes Principal Component Analysis (PCA) in such a way that reduces the variance of the PCA scores. In order to do so, DRR relies on a deflationary process in which a non-linear regression reduces the redundancy between the PC scores. Unlike other nonlinear dimensionality reduction methods, DRR is easy to apply, it has out-of-sample extension, it is invertible, and the learned transformation is volume-preserving. these properties make the method useful for a wide range of applications, especially in very high dimensional data in general, and for hyperspectral image processing in particular. We illustrate the performance of the algorithm in reducing the dimensionality of IASI hyperspectral image sounding data. We compare DRR with related and invertible methods such as linear PCA and Principal Polynomial Analysis (PPA) in terms of reconstruction error, and expressive power of the extracted features to estimate atmospheric variables.

关键词： Manifold learning nonlinear dimensionality reduction principal component analysis Principal Polynomial Analysis hyperspectral sounder MetOp IASI

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VALIDATING nonlinear MIXING MODELS: BENCHMARK DATASETS FROM VEGETATED AREAS 6

VALIDATING NONLINEAR MIXING MODELS: BENCHMARK DATASETS FROM ...

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6th workshop on Hyperspectral image and signal processing - Evolution in Remote Sensing (WHISPERS)

作者： Tits, L. Somers, B. Stuckens, J. Coppin, P. Katholieke Univ Leuven Dept Biosyst Geomat Lab BE-3001 Leuven Belgium Katholieke Univ Leuven Div Forest Nat & Landscape BE-3001 Leuven Belgium Merkator NV BE-1731 Zellik Belgium

ISBN: (纸本)9781467390125

Our understanding of nonlinear mixing events in vegetated areas is currently hampered by a pertinent lack of well-validated datasets. Most quantification and modeling efforts are based on theoretical assumptions or indirect empirical observations. In this study, a physically based ray tracer was used to create simulated hyperspectral datasets of vegetative systems. this model incorporates multiple scattering effects, and nonlinear mixing behavior can be observed in the rendered data. the main benefit of the ray-tracer is that we were able to demonstrate with in situ measurements that both the nature and the intensity of the nonlinear mixing events are realistically modeled. Different ray-tracer datasets will be made available to the wider scientific community as a benchmark dataset to test and validate new and existing unmixing methodologies. In this contribution, we would like to present the structure of these datasets, and show how they can be used to evaluate nonlinear mixing models. In addition, and maybe even more important, we would like to draw the attention to the limitations of the data, as well point out the assumptions made in the construction of the data.

关键词： Hyperspectral ray-tracer nonlinear mixing vegetative systems

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Piecewise linear model-based image enhancement

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EURASIP JOURNAL ON APPLIED signal processing 2004年第12期2004卷 1861-1869页

作者： Russo, F Univ Trieste Dept Elect Elect & Comp Engn I-34127 Trieste Italy

A novel technique for the sharpening of noisy images is presented. the proposed enhancement system adopts a simple piecewise linear (PWL) function in order to sharpen the image edges and to reduce the noise. Such effects can easily be controlled by varying two parameters only. the noise sensitivity of the operator is further decreased by means of an additional filtering step, which resorts to a nonlinear model too. Results of computer simulations show that the proposed sharpening system is simple and effective. the application of the method to contrast enhancement of color images is also discussed.

关键词： image enhancement sharpening noise reduction nonlinear filters

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nonlinear UNMIXING OF VEGETATED AREAS: A MODEL COMPARISON BASED ON SIMULATED AND REAL HYPERSPECTRAL DATA 6

NONLINEAR UNMIXING OF VEGETATED AREAS: A MODEL COMPARISON BA...

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6th workshop on Hyperspectral image and signal processing - Evolution in Remote Sensing (WHISPERS)

作者： Dobigeon, Nicolas Tits, Laurent Somers, Ben Altmann, Yoann Coppin, Pol Univ Toulouse IRIT INP ENSEEIHT TeSA Toulouse France Katholieke Univ Leuven Dept Biosyst Leuven Belgium Katholieke Univ Leuven Div Forest Nat & Landscape Leuven Belgium Heriot Watt Univ Sch Engn & Phys Sci Edinburgh Midlothian Scotland

ISBN: (纸本)9781467390125

When analyzing remote sensing hyperspectral images, numerous works dealing with spectral unmixing assume the pixels result from linear combinations of the endmember signatures. However, this assumption cannot be fulfilled, in particular when considering images acquired over vegetated areas. As a consequence, several nonlinear mixing models have been recently derived to take various nonlinear effects into account when unmixing hyperspectral data. Unfortunately, these models have been empirically proposed and without thorough validation. this paper attempts to fill this gap by taking advantage of two sets of real and physical-based simulated data. the accuracy of various linear and nonlinear models and the corresponding unmixing algorithms is evaluated with respect to their ability of fitting the sensed pixels and of providing accurate estimates of the abundances.

关键词： Hyperspectral imagery spectral unmixing nonlinear spectral mixtures vegetated areas ray tracing

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nonlinear signal and image processing-a special issue in honour of Giovanni L. Sicuranza on his seventy-fifth birthday

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EURASIP JOURNAL ON ADVANCES IN signal processing 2017年第1期2017卷 1-3页

作者： Carini, Alberto Ramponi, Giovanni Mathews, V. John Univ Urbino DiSPeA Piazza Repubbl 13 I-61029 Urbino Italy Univ Trieste DIA Via A Valerio 10 I-34127 Trieste Italy Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA

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SPATIALLY AWARE SUPERVISED nonlinear DIMENSIONALITY REDUCTION FOR HYPERSPECTRAL DATA 6

SPATIALLY AWARE SUPERVISED NONLINEAR DIMENSIONALITY REDUCTIO...

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6th workshop on Hyperspectral image and signal processing - Evolution in Remote Sensing (WHISPERS)

作者： Volpi, Michele Tuia, Devis Univ Edinburgh Sch Informat IPAB CALVIN Grp Edinburgh EH8 9AB Midlothian Scotland Ecole Polytech Fed Lausanne LaSIG CH-1015 Lausanne Switzerland

ISBN: (纸本)9781467390125

In this paper we study the effect of injecting spatial information of image patches directly in the process of supervised dimensionality reduction. In particular, we adopt an approach derived from the mean map kernel framework to map image patches of variable size into a reproducing kernel Hilbert space. In that space, the orthonormalized partial least squares performs supervised dimensionality reduction to a discriminant subspace. Advantages of the proposed approach are discussed by studying two well known hyperspectral image benchmarks and by comparing it to composite-kernel feature extraction framework.

关键词： Supervised feature extraction Dimensionality reduction Spatial information Kernels

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