Prof. Jon Atli Benediktsson Profile

Prof. Jon Atli Benediktsson

Professor at Univ of Iceland

SPIE Involvement:

Author | Editor

Websites:

Personal Website

Publications (23)

SPIE Journal Paper | 13 December 2022

Remote sensing image fusion based on NSST and parameter adaptive PCNN

Yuetao Pan, Danfeng Liu, Liguo Wang, Jón Atli Benediktsson, Shishuai Xing

JARS, Vol. 16, Issue 04, 044519, (December 2022) https://doi.org/10.1117/12.10.1117/1.JRS.16.044519

KEYWORDS: Image fusion, Remote sensing, Image enhancement, Image processing, Distortion, Tunable filters, Spatial resolution, Principal component analysis, Wavelets, Silver

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SPIE Journal Paper | 31 March 2021

Adaptive region-based post-classification framework for land-cover mapping improvement using very high spatial resolution optical imagery

Zhiyong Lv, GuangFei Li, JiXing Yan, Jón Atli Benediktsson

JARS, Vol. 15, Issue 01, 014524, (March 2021) https://doi.org/10.1117/12.10.1117/1.JRS.15.014524

KEYWORDS: Image classification, Remote sensing, Orthophoto maps, Image filtering, Visualization, Spatial resolution, Adaptive optics, Satellites, Satellite imaging, Earth observing sensors

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SPIE Journal Paper | 3 July 2019

Training sample refining method using an adaptive neighbor to improve the classification performance of very high-spatial resolution remote sensing images

ZhiYong Lv, GuangFei Li, Jón Atli Benediktsson, Zhou Zhang, JiXing Yan

JARS, Vol. 13, Issue 03, 036501, (July 2019) https://doi.org/10.1117/12.10.1117/1.JRS.13.036501

KEYWORDS: Image classification, Remote sensing, Image resolution, Visualization, Rutherfordium, Reliability, Lithium, Spatial resolution, Feature extraction, Roads

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Proceedings Article | 23 October 2014 Paper

Feature selection of hyperspectral data by considering the integration of genetic algorithms and particle swarm optimization

Pedram Ghamisi, Jon Atli Benediktsson

Proceedings Volume 9244, 92440J (2014) https://doi.org/10.1117/12.2065472

KEYWORDS: Feature selection, Particle swarm optimization, Genetic algorithms, Data acquisition, Particles, Stochastic processes, Hyperspectral imaging, Statistical analysis, Computer engineering, Astatine

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Proceedings Article | 17 October 2013 Paper

Classification of hyperspectral images with binary fractional order Darwinian PSO and random forests

Pedram Ghamisi, Micael Couceiro, Jon Benediktsson

Proceedings Volume 8892, 88920S (2013) https://doi.org/10.1117/12.2027641

KEYWORDS: Particles, Image classification, Particle swarm optimization, Hyperspectral imaging, Binary data, Optimization (mathematics), Calculus, Remote sensing, Feature selection, Classification systems

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Proceedings Article | 17 October 2013 Paper

Hyperspectral image restoration using wavelets

Behnood Rasti, Johannes Sveinsson, Magnus Ulfarsson, Jon Benediktsson

Proceedings Volume 8892, 889207 (2013) https://doi.org/10.1117/12.2029240

KEYWORDS: Hyperspectral imaging, Wavelets, Signal to noise ratio, Image restoration, Principal component analysis, Visualization, Denoising, Feature extraction, Hyperspectral simulation, Convex optimization

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Proceedings Article | 17 October 2013 Paper

Wavelet based hyperspectral image restoration using spatial and spectral penalties

Behnood Rasti, Johannes Sveinsson, Magnus Ulfarsson, Jon Benediktsson

Proceedings Volume 8892, 88920I (2013) https://doi.org/10.1117/12.2029257

KEYWORDS: Wavelets, Signal to noise ratio, Hyperspectral imaging, Image restoration, Visualization, Hyperspectral simulation, Principal component analysis, Sensors, 3D image processing, Atmospheric modeling

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Proceedings Article | 17 October 2013 Paper

Extraction of spatial features in hyperspectral images based on the analysis of differential attribute profiles

Nicola Falco, Jon Benediktsson, Lorenzo Bruzzone

Proceedings Volume 8892, 88920O (2013) https://doi.org/10.1117/12.2029199

KEYWORDS: Electroactive polymers, Feature extraction, Hyperspectral imaging, Principal component analysis, Remote sensing, Sensors, Spatial resolution, Mathematical morphology, Data modeling, Image sensors

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The new generation of hyperspectral sensors can provide images with a high spectral and spatial resolution. Recent improvements in mathematical morphology have developed new techniques such as the Attribute Profiles (APs) and the Extended Attribute Profiles (EAPs) that can effectively model the spatial information in remote sensing images. The main drawbacks of these techniques is the selection of the optimal range of values related to the family of criteria adopted to each filter step, and the high dimensionality of the profiles, which results in a very large number of features and therefore provoking the Hughes phenomenon. In this work, we focus on addressing the dimensionality issue, which leads to an highly intrinsic information redundancy, proposing a novel strategy for extracting spatial information from hyperspectral images based on the analysis of the Differential Attribute Profiles (DAPs). A DAP is generated by computing the derivative of the AP; it shows at each level the residual between two adjacent levels of the AP. By analyzing the multilevel behavior of the DAP, it is possible to extract geometrical features corresponding to the structures within the scene at different scales. Our proposed approach consists of two steps: 1) a homogeneity measurement is used to identify the level L in which a given pixel belongs to a region with a physical meaning; 2) the geometrical information of the extracted regions is fused into a single map considering their level L previously identified. The process is repeated for different attributes building a reduced EAP, whose dimensionality is much lower with respect to the original EAP ones. Experiments carried out on the hyperspectral data set of Pavia University area show the effectiveness of the proposed method in extracting spatial features related to the physical structures presented in the scene, achieving higher classification accuracy with respect to the ones reported in the state-of-the-art literature

Proceedings Article | 8 November 2012 Paper

Extending the fractional order Darwinian particle swarm optimization to segmentation of hyperspectral images

Pedram Ghamisi, Micael Couceiro, Jon Atli Benediktsson

Proceedings Volume 8537, 85370F (2012) https://doi.org/10.1117/12.978776

KEYWORDS: Image segmentation, Particle swarm optimization, Particles, Hyperspectral imaging, Calculus, Remote sensing, Optimization (mathematics), Sensors, Image processing algorithms and systems, Image analysis

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Proceedings Article | 8 November 2012 Paper

Detection of hedges based on attribute filters

Gabrielle Cavallaro, Benoit Arbelot, Mathieu Fauvel, Mauro Dalla Mura, Jón Atli Benediktsson, Lorenzo Bruzzone, Jocelyn Chanussot, David Sheeren

Proceedings Volume 8537, 853712 (2012) https://doi.org/10.1117/12.999360

KEYWORDS: Image filtering, Image processing, Image fusion, Sensors, Mathematical morphology, Image analysis, Multispectral imaging, Image quality, Environmental monitoring, Environmental sensing

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Proceedings Article | 19 October 2012 Paper

Spectral unmixing of multispectral satellite images with dimensionality expansion using morphological profiles

Sergio Bernabé, Prashanth Marpu, Antonio Plaza, Jon Benediktsson

Proceedings Volume 8514, 85140Z (2012) https://doi.org/10.1117/12.930418

KEYWORDS: RGB color model, Multispectral imaging, Satellite imaging, Satellites, Earth observing sensors, Hyperspectral imaging, Remote sensing, Feature extraction, Image processing, Image classification

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Proceedings Article | 27 October 2011 Paper

Spectral-spatial classification of polarimetric SAR data using morphological attribute profiles

Prashanth Reddy Marpu, Kun-Shan Chen, Jon Alti Benediktsson

Proceedings Volume 8180, 81800K (2011) https://doi.org/10.1117/12.898008

KEYWORDS: Electroactive polymers, Polarimetry, Synthetic aperture radar, Polarization, Remote sensing, Selenium, Scattering, Image classification, Roads, Data analysis

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Proceedings Article | 27 October 2011 Paper

Fusion of hyperspectral and lidar data using morphological attribute profiles

Mattia Pedergnana, Prashanth Marpu, Mauro Dalla Mura, Jon Atli Benediktsson, Lorenzo Bruzzone

Proceedings Volume 8180, 81801G (2011) https://doi.org/10.1117/12.899294

KEYWORDS: LIDAR, Electroactive polymers, Buildings, Hyperspectral imaging, Principal component analysis, Data fusion, Roads, Image fusion, Remote sensing, Data acquisition

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Proceedings Article | 23 October 2010 Paper

Supervised super-resolution to improve the resolution of hyperspectral images classification maps

Alberto Villa, Jocelyn Chanussot, Jon Atli Benediktsson, Christian Jutten

Proceedings Volume 7830, 78300J (2010) https://doi.org/10.1117/12.864938

KEYWORDS: Spatial resolution, Hyperspectral imaging, Image classification, Image resolution, Algorithms, Statistical analysis, Image segmentation, Super resolution, Spectral resolution, Remote sensing

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Hyperspectral imaging is a continuously growing area of remote sensing. Hyperspectral data provide a wide spectral range, coupled with a very high spectral resolution, and are suitable for detection and classification of surfaces and chemical elements in the observed image. The main problem with hyperspectral data for these applications is the (relatively) low spatial resolution, which can vary from a few to tens of meters. In the case of classification purposes, the major problem caused by low spatial resolution is related to mixed pixels, i.e., pixels in the image where more than one land cover class is within the same pixel. In such a case, the pixel cannot be considered as belonging to just one class, and the assignment of the pixel to a single class will inevitably lead to a loss of information, no matter what class is chosen. In this paper, a new supervised technique exploiting the advantages of both probabilistic classifiers and spectral unmixing algorithms is proposed, in order to produce land cover maps of improved spatial resolution. The method is in three steps. In a first step, a coarse classification is performed, based on the probabilistic output of a Support Vector Machine (SVM). Every pixel can be assigned to a class, if the probability value obtained in the classification process is greater than a chosen threshold, or unclassified. In the proposed approach it is assumed that the pixels with a low probabilistic output are mixed pixels and thus their classification is addressed in a second step. In the second step, spectral unmixing is performed on the mixed pixels by considering the preliminary results of the coarse classification step and applying a Fully Constrained Least Squares (FCLS) method to every unlabeled pixel, in order to obtain the abundances fractions of each land cover type. Finally, in a third step, spatial regularization by Simulated Annealing is performed to obtain the resolution improvement. Experiments were carried out on a real hyperspectral data set. The results are good both visually and numerically and show that the proposed method clearly outperforms common hard classification methods when the data contain mixed pixels.

Proceedings Article | 23 October 2010 Paper

Study on the capabilities of morphological attribute profiles in change detection on VHR images

Nicola Falco, Mauro Dalla Mura, Francesca Bovolo, Jon Atli Benediktsson, Lorenzo Bruzzone

Proceedings Volume 7830, 783016 (2010) https://doi.org/10.1117/12.866178

KEYWORDS: Image filtering, Image resolution, Sensors, Image analysis, Earthquakes, Spatial resolution, Remote sensing, Filtering (signal processing), Data acquisition, Computer engineering

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Proceedings Article | 23 October 2010 Paper

Alternating sequential filters with morphological attribute operators for the analysis of remote sensing images

Mauro Dalla Mura, Jon Atli Benediktsson, Lorenzo Bruzzone

Proceedings Volume 7830, 783006 (2010) https://doi.org/10.1117/12.866232

KEYWORDS: Image filtering, Image processing, Image resolution, Remote sensing, Filtering (signal processing), Roads, Image analysis, Selenium, Buildings, Computer engineering

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Proceedings Article | 28 September 2009 Paper

Spectral-spatial analysis in hyperspectral remote sensing: from morphological profiles to classified segmentation

Jocelyn Chanussot, Jon-Atli Benediktsson, Mathieu Fauvel, Yuliya Tarabalka

Proceedings Volume 7477, 74770E (2009) https://doi.org/10.1117/12.836023

KEYWORDS: Image segmentation, Image classification, Hyperspectral imaging, Remote sensing, Expectation maximization algorithms, Composites, Mathematical morphology, Image processing algorithms and systems, Radon, Data fusion

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Proceedings Article | 28 September 2009 Paper

Modeling structural information for building extraction with morphological attribute filters

M. Dalla Mura, J. Benediktsson, L. Bruzzone

Proceedings Volume 7477, 747703 (2009) https://doi.org/10.1117/12.836021

KEYWORDS: Image filtering, Image processing, Data modeling, Genetic algorithms, Image classification, Vegetation, Image resolution, Optimization (mathematics), Mathematical morphology, Feature extraction

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Proceedings Article | 19 October 2005 Paper

Classification of remote sensing imagery with high spatial resolution

Mathieu Fauvel, Jon Aevar Palmason, Jon Atli Benediktsson, Jocelyn Chanussot, Johannes Sveinsson

Proceedings Volume 5982, 598201 (2005) https://doi.org/10.1117/12.637224

KEYWORDS: Feature extraction, Image classification, Neural networks, Remote sensing, Independent component analysis, Spatial resolution, Principal component analysis, Mathematical morphology, Fuzzy logic, Image resolution

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Proceedings Article | 28 January 2002 Paper

Review of applications of wavelets in speckle reduction and enhancement of SAR images

Johannes Sveinsson, Jon Benediktsson

Proceedings Volume 4541, (2002) https://doi.org/10.1117/12.454179

KEYWORDS: Wavelets, Synthetic aperture radar, Speckle, Denoising, Interference (communication), Image processing, Image filtering, Discrete wavelet transforms, Wavelet transforms, Filtering (signal processing)

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Proceedings Article | 14 December 1999 Paper

Neuro-fuzzy and soft computing in classification of remote sensing data

Jon Benediktsson, Helgi Benediktsson

Proceedings Volume 3871, (1999) https://doi.org/10.1117/12.373255

KEYWORDS: Fuzzy logic, Neural networks, Genetic algorithms, Remote sensing, Neurons, Fuzzy systems, Optimization (mathematics), Intelligence systems, Data modeling, Systems modeling

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Proceedings Article | 14 December 1999 Paper

Speckle reduction and enhancement of SAR images using multiwavelets and adaptive thresholding

Johannes Sveinsson, Jon Benediktsson

Proceedings Volume 3871, (1999) https://doi.org/10.1117/12.373262

KEYWORDS: Synthetic aperture radar, Speckle, Wavelets, Denoising, Matrices, Image processing, Filtering (signal processing), Information operations, Interference (communication), Image analysis

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Proceedings Article | 4 December 1998 Paper

Optimized combination, regularization, and pruning in parallel consensual neural networks

Jon Benediktsson, Jan Larsen, Johannes Sveinsson, L. Hansen

Proceedings Volume 3500, (1998) https://doi.org/10.1117/12.331874

KEYWORDS: Neural networks, Data modeling, Remote sensing, Neurons, Network architectures, Brain, Error analysis, Synthetic aperture radar, Wavelets, Statistical methods

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Showing 5 of 23 publications

Proceedings Volume Editor (21)

SPIE Conference Volume | 27 November 2023

Image and Signal Processing for Remote Sensing XXIX

SPIE Conference Volume | 4 November 2022

Image and Signal Processing for Remote Sensing XXVIII

SPIE Conference Volume | 6 October 2021

Image and Signal Processing for Remote Sensing XXVII

SPIE Conference Volume | 9 October 2020

Image and Signal Processing for Remote Sensing XXVI

SPIE Conference Volume | 3 December 2019

Image and Signal Processing for Remote Sensing XXV

Showing 5 of 21 publications

Conference Committee Involvement (22)

Image and Signal Processing for Remote Sensing XXIX

4 September 2023 | Amsterdam, Netherlands

Image and Signal Processing for Remote Sensing XXVIII

5 September 2022 | Berlin, Germany

Image and Signal Processing for Remote Sensing XXVII

13 September 2021 | Online Only, Spain

Image and Signal Processing for Remote Sensing XXVI

21 September 2020 | Online Only, United Kingdom

Image and Signal Processing for Remote Sensing XXV

9 September 2019 | Strasbourg, France