Institute for Space Applications and Remote Sensing

Pentéli, Greece

Institute for Space Applications and Remote Sensing

Pentéli, Greece
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Katselis D.,National and Kapodistrian University of Athens | Kofidis E.,University of Piraeus | Rontogiannis A.,Institute for Space Applications and Remote Sensing | Theodoridis S.,National and Kapodistrian University of Athens
IEEE Transactions on Signal Processing | Year: 2010

In this correspondence, preamble-based least squares (LS) channel estimation in orthogonal frequency division multiplexing (OFDM) systems of the QAM and offset QAM (OQAM) types is considered. The construction of optimal (in the mean squared error (MSE) sense) preambles is investigated, for sparse (a subset of pilot tones, surrounded by nulls) preambles. The two OFDM systems are compared for the same transmit power, which, for cyclic prefix (CP) based OFDM/QAM, also includes the power spent for CP transmission. OFDM/OQAM, with a sparse preamble consisting of equipowered and equispaced pilots embedded in zeros, turns out to perform at least as well as CP-OFDM. Simulations results are presented that verify the analysis. © 2006 IEEE.


Themelis K.E.,National and Kapodistrian University of Athens | Themelis K.E.,Institute for Space Applications and Remote Sensing | Rontogiannis A.A.,Institute for Space Applications and Remote Sensing | Koutroumbas K.D.,Institute for Space Applications and Remote Sensing
IEEE Transactions on Signal Processing | Year: 2012

In this paper the problem of semisupervised hyperspectral unmixing is considered. More specifically, the unmixing process is formulated as a linear regression problem, where the abundance's physical constraints are taken into account. Based on this formulation, a novel hierarchical Bayesian model is proposed and suitable priors are selected for the model parameters such that, on the one hand, they ensure the nonnegativity of the abundances, while on the other hand they favor sparse solutions for the abundances' vector. Performing Bayesian inference based on the proposed hierarchical Bayesian model, a new low-complexity iterative method is derived, and its connection with Gibbs sampling and variational Bayesian inference is highlighted. Experimental results on both synthetic and real hyperspectral data illustrate that the proposed method converges fast, favors sparsity in the abundances' vector, and offers improved estimation accuracy compared to other related methods. © 2011 IEEE.


Retalis A.,Institute for Environmental Research and Sustainable Development | Sifakis N.,Institute for Space Applications and Remote Sensing
ISPRS Journal of Photogrammetry and Remote Sensing | Year: 2010

Low and moderate spatial resolution satellite sensors (such as TOMS, AVHRR, SeaWiFS) have already shown their capability in tracking aerosols at a global scale. Sensors with moderate to high spatial resolution (such as MODIS and MERIS) seem also to be appropriate for aerosol retrieval at a regional scale. We investigated in this study the potential of MERIS-ENVISAT data to resolve the horizontal spatial distribution of aerosols over urban areas, such as the Athens metropolitan area, by using the differential textural analysis (DTA) code. The code was applied to a set of geo-corrected images to retrieve and map aerosol optical thickness (AOT) values relative to a reference image assumed to be clean of pollution with a homogeneous atmosphere. The comparison of satellite retrieved AOT against PM10 data measured at ground level showed a high positive correlation particularly for the AOT values calculated using the 5th MERIS' spectral band (R2=0.83). These first results suggest that the application of the DTA code on cloud free areas of MERIS images can be used to provide AOT related to air quality in this urban region. The accuracy of retrieved AOT mainly depends on the overall quality, the pollution cleanness and the atmospheric homogeneity of the reference image. © 2009 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).


Keramitsoglou I.,Institute for Space Applications and Remote Sensing | Kiranoudis C.T.,National Technical University of Athens | Ceriola G.,Planetek Italia S.r.l. | Weng Q.,Indiana State University | Rajasekar U.,Indiana State University
Remote Sensing of Environment | Year: 2011

Thermal infrared images are being acquired by satellites for more than two decades enabling studies of the human-induced Urban Heat Island (UHI) phenomenon. As a result, the requirement of the scientific community for fast and efficient methods for extracting and analyzing the thermal patterns from a vast volume of acquired data has emerged. The present paper proposes an innovative object-based image analysis procedure to extract thermal patterns for the quantitative analysis of satellite-derived Land Surface Temperature (LST) maps. The spatial and thermal attributes associated with these objects are then calculated and used for the analyses of the intensity, the position and the spatial extent of UHIs. A case study was conducted in the Greater Athens Area, Greece. More than 3000 LST images of the area acquired by MODIS sensor over a decade were analyzed. Three daytime hot-spots were identified and studied (Megara, Elefsina-Aspropyrgos and Mesogeia). They were all found to exhibit similar behavior, gradually increasing their maximum temperature during the summer season and reaching their maxima in mid-July. The hot-spots' thermal intensities compared to a suburban area were of 9-10°C and were found to be highly correlated to their areal extent. During the night-time, Athens center developed a typical UHI spatially coinciding with the dense urban fabric. The nighttime maximum LST peaked (on average) at the end of July, two weeks later than the daytime surface patterns. The mean spatial extent of UHI in Athens was 55.2km2, whilst its mean intensity was 5.6°C. The proposed automatic extraction process can be customized for other cities and potentially used for comparison of LST patterns and UHI behavior between different cities. © 2011 Elsevier Inc.


Lalos A.S.,University of Patras | Rontogiannis A.A.,Institute for Space Applications and Remote Sensing | Berberidis K.,University of Patras
IEEE Transactions on Signal Processing | Year: 2010

In this correspondence, we deal with the problem of channel estimation in amplify-and-forward (AF) wideband cooperative relay-based networks. Two types of frequency domain channel estimation techniques are proposed and analyzed. First, a training based technique is presented for which an optimal pilot placement and power allocation strategy is described. Second, hybrid techniques are introduced in which both training as well as channel output correlation information is utilized for channel estimation. A theoretical performance study of the proposed algorithms is presented and closed-form expressions for the mean squared channel estimation error are provided. The presented theoretical analysis is verified via extensive Monte Carlo simulations. © 2010 IEEE.


Mandellos N.A.,National Technical University of Athens | Keramitsoglou I.,Institute for Space Applications and Remote Sensing | Kiranoudis C.T.,National Technical University of Athens
Expert Systems with Applications | Year: 2011

An innovative system for detecting and extracting vehicles in traffic surveillance scenes is presented. This system involves locating moving objects present in complex road scenes by implementing an advanced background subtraction methodology. The innovation concerns a histogram-based filtering procedure, which collects scatter background information carried in a series of frames, at pixel level, generating reliable instances of the actual background. The proposed algorithm reconstructs a background instance on demand under any traffic conditions. The background reconstruction algorithm demonstrated a rather robust performance in various operating conditions including unstable lighting, different view-angles and congestion. © 2010 Elsevier Ltd. All rights reserved.


Grosso N.,New University of Lisbon | Paronis D.,Institute for Space Applications and Remote Sensing
Atmospheric Research | Year: 2012

Current satellite aerosol retrieval products could be complemented by contrast reduction methods to overcome limitations regarding highly reflective or heterogeneous surfaces such as urban, desert or snow covered areas. Algorithms based on the contrast reduction principle, define contrast loss in an image, inside a pre-determined window size, as an exponential function of the Aerosol Optical Thickness (AOT) difference between two images (a reference and a polluted) acquired under similar observation geometry conditions. This paper presents a contrast reduction algorithm designed for the MODIS sensor, based on the Differential Texture Analysis (DTA) approach. It focuses on algorithm optimization by: a) determining an optimal AOT spatial resolution; b) constraining the relative observation geometry differences between polluted and reference images; and c) assessing the influence of several land cover classes on the accuracy of the retrievals. A comparison of the results obtained for 192 images acquired for the year 2005 with data from five European AERONET stations is performed to assess overall algorithm accuracy as well as the impact of the proposed improvements. Comparative analysis of the results for the various sites showed an optimal algorithm performance for MODIS images using a 39. pixel distance window, composed of only forest and urban pixels. Comparison with AERONET AOT data showed a good agreement with a correlation coefficient of 0.78. A similar correlation is found when comparing AERONET measurements and MODIS aerosol standard product. This research supports the establishment of contrast reduction methods as a potential complement to other aerosol retrieval methodologies. Future work will aim at removing the residual aerosol influence from reference images, including BRDFs to better reproduce surface heterogeneity and observation geometry influences and expanding the scope of this study to other AERONET sites so as to further test the algorithm at a global scale. © 2011 Elsevier B.V.


Petropoulos G.P.,Aberystwyth University | Kontoes C.C.,Institute for Space Applications and Remote Sensing | Keramitsoglou I.,Institute for Space Applications and Remote Sensing
International Journal of Applied Earth Observation and Geoinformation | Year: 2012

In this study, the potential of EO-1 Advanced Land Imager (ALI) radiometer for land cover and especially burnt area mapping from a single image analysis is investigated. Co-orbital imagery from the Landsat Thematic Mapper (TM) was also utilised for comparison purposes. Both images were acquired shortly after the suppression of a fire occurred during the summer of 2009 North-East of Athens, the capital of Greece. The Maximum Likelihood (ML), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) classifiers were parameterised and subsequently applied to the acquired satellite datasets. Evaluation of the land use/cover mapping accuracy was based on the error matrix statistics. Also, the McNemar test was used to evaluate the statistical significance of the differences between the approaches tested. Derived burnt area estimates were validated against the operationally deployed Services and Applications For Emergency Response (SAFER) Burnt Scar Mapping service. All classifiers applied to either ALI or TM imagery proved flexible enough to map land cover and also to extract the burnt area from other land surface types. The highest total classification accuracy and burnt area detection capability was returned from the application of SVMs to ALI data. This was due to the SVMs ability to identify an optimal separating hyperplane for best classes' separation that was able to better utilise ALI's advanced technological characteristics in comparison to those of TM sensor. This study is to our knowledge the first of its kind, effectively demonstrating the benefits of the combined application of SVMs to ALI data further implying that ALI technology may prove highly valuable in mapping burnt areas and land use/cover if it is incorporated into the development of Landsat 8 mission, planned to be launched in the coming years. © 2012 Elsevier B.V.


Sykioti O.,Institute for Space Applications and Remote Sensing | Paronis D.,Institute for Space Applications and Remote Sensing | Stagakis S.,University of Ioannina | Kyparissis A.,University of Ioannina
Remote Sensing of Environment | Year: 2011

The absorption feature approach was used in CHRIS multiangular hyperspectral data in order to investigate its potential for ecosystem remote sensing. For that purpose, CHRIS images in mode 1 were acquired throughout a two-year period for a Mediterranean ecosystem dominated by the semi-deciduous shrub Phlomis fruticosa. During each acquisition, coincident in situ Leaf spectra and ecophysiological measurements (Leaf Area Index, leaf pigment content and leaf water potential) were conducted. After data preprocessing, absorption feature information was calculated for both CHRIS and Leaf spectra for the whole spectrum. Three common characteristic absorption features within the spectral areas 450-550. nm, 550-750. nm and 900-1000. nm were detected. Each spectral area was then examined separately and four characteristic parameters were calculated that described the pattern, magnitude and position of the maximum absorption. Correlations between CHRIS and Leaf spectra for each date and viewing angle (VA) were then conducted. All correlations, either on full continuum removed spectra or on spectral areas, showed high coefficients of determination, especially (i) in higher observation angles (VA +. 55), (ii) during the wet season and (iii) in strong absorptions such as the "red absorption". Subsequently, correlations between CHRIS and Leaf absorption parameters of selected spectral areas with field-measured ecophysiological parameters were examined. Ecophysiological parameters proved to be highly correlated to CHRIS and Leaf absorption parameters in magnitude and/or pattern of the absorption feature and less in wavelength of the maximum absorption. CHRIS VAs +/- 36 showed the highest correlations although the type of relation, linear or nonlinear, was not conclusive. Finally, a first comparison between narrowband spectral indices and absorption features in correlations with ecophysiological parameters showed that both methods provide significant and comparable results, with oblique angles showing best performance. However, ecophysiological parameters are generally better predicted linearly by narrowband spectral indices issued from CHRIS, with most significant differences appearing on pigments absorbing mainly within 450-550. nm. © 2010 Elsevier Inc.


Stagakis S.,University of Ioannina | Markos N.,University of Ioannina | Sykioti O.,Institute for Space Applications and Remote Sensing | Kyparissis A.,University of Ioannina
Remote Sensing of Environment | Year: 2010

This study focuses on the potential of satellite hyperspectral imagery to monitor vegetation biophysical and biochemical characteristics through narrow-band indices and different viewing angles. Hyperspectral images of the CHRIS/PROBA sensor in imaging mode 1 (5 observation angles, 62 bands, 410-1005 nm) were acquired throughout a two-year period for a Mediterranean ecosystem fully covered by the semi-deciduous shrub Phlomis fruticosa. During each acquisition, coincident ecophysiological field measurements were conducted. Leaf area index (LAI), leaf biochemical content (chlorophyll a, chlorophyll b, carotenoids) and leaf water potential were measured. The hyperspectral images were corrected for coherent noises, cloud and atmosphere, in order to produce ground reflectance images. The reflectance spectrum of each image was used to calculate a variety of vegetation indices (VIs) that are already published in relevant literature. Additionally, all combinations of the 62 bands were used in order to calculate Normalized Difference Spectral Indices (NDSI(x,y)) and Simple Subtraction Indices (SSI(x,y)). The above indices along with raw reflectance and reflectance derivatives were examined for linear relationship with the ground-measured variables and the strongest relationships were determined. It is concluded that higher observation angles are better for the extraction of biochemical indices. The first derivative of the reflectance spectra proved to be very useful in the prediction of all measured variables. In many cases, complex and improved spectral indices that are proposed in the literature do not seem to be more accurate than simple NDSIs such as NDVI. Even traditional broadband NDVI is proved to be adequate in LAI prediction, while green bands seem also very useful. However, in biochemical estimation narrow bands are necessary. Indices that incorporate red, blue and IR bands, such as PSRI, SIPI and mNDVI presented good performance in chlorophyll estimation, while CRI did not show any relevance to carotenoids and WI was poorly correlated to water potential. Moreover, analyses indicated that it is very important to use a near red-edge band (701 nm) for effective chlorophyll index design. SSIs that incorporate 701 nm with 511 or 605 nm showed best performance in chlorophyll determination. For carotenoid estimation, a band on the edge of carotenoid absorption (511 nm) combined with a red band performed best, while a normalized index of two water absorption bands (945, 971 nm) proved to be an effective water index. Finally, the attempt to investigate stress conditions through pigment ratios resulted in the use of the band centred at 701 nm. © 2010 Elsevier Inc. All rights reserved.

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