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	<title>GRSS &#124; IEEE &#124; Geoscience &#38; Remote Sensing Society</title>
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		<title>Localized Registration of Point Clouds of Botanic Trees</title>
		<link>http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6331508</link>
		<comments>http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6331508#comments</comments>
		<pubDate>Wed, 01 May 2013 00:00:00 +0000</pubDate>
		<dc:creator>Geoscience and Remote Sensing Letters, IEEE - new TOC</dc:creator>
				<category><![CDATA[Letters]]></category>
		<category><![CDATA[syndication]]></category>

		<guid isPermaLink="false">http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6331508</guid>
		<description><![CDATA[A global registration is often insufficient for estimating dendrometric characteristics of trees because individual branches of the same tree may exhibit different positions between two scanning procedures. Therefore, we introduce a localized approach ...]]></description>
			<content:encoded><![CDATA[A global registration is often insufficient for estimating dendrometric characteristics of trees because individual branches of the same tree may exhibit different positions between two scanning procedures. Therefore, we introduce a localized approach to register point clouds of botanic trees. Given two roughly registered point clouds <formula formulatype="inline"><tex Notation="TeX">$hbox{PC}_{1}$</tex></formula> and <formula formulatype="inline"><tex Notation="TeX">$ hbox{PC}_{2}$</tex></formula> of a tree, we apply a skeletonization method to both point clouds. Based on these two skeletons, initial correspondences between branch segments of both point clouds are established to estimate local transformation parameters. The transformation estimation relies on minimizing the distance between the points in <formula formulatype="inline"><tex Notation="TeX">$hbox{PC}_{1}$</tex></formula> and the skeleton of <formula formulatype="inline"> <tex Notation="TeX">$hbox{PC}_{2}$</tex></formula>. The performance of the method is demonstrated on two example trees. It is shown that significant improvements can be achieved for the registration of fine branches. These improvements are quantified as the residual point-to-line distances before and after the localized fine registration. In our experiment, the residual error after the local registration is on an average of 5 mm over 90 skeleton segments, which is about three times smaller than the average residual error of the initial rough registration.]]></content:encoded>
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		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Sea Surface Salinity Estimation in the Bay of Bengal Using Multisatellite Measurements</title>
		<link>http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6302174</link>
		<comments>http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6302174#comments</comments>
		<pubDate>Wed, 01 May 2013 00:00:00 +0000</pubDate>
		<dc:creator>Geoscience and Remote Sensing Letters, IEEE - new TOC</dc:creator>
				<category><![CDATA[Letters]]></category>
		<category><![CDATA[syndication]]></category>

		<guid isPermaLink="false">http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6302174</guid>
		<description><![CDATA[An algorithm is developed to estimate sea surface salinity (SSS) from the combined use of outgoing longwave radiation and freshwater flux derived by the First Generation Meteosat Visible and InfraRed Imager and Tropical Rainfall Measuring Mission data ...]]></description>
			<content:encoded><![CDATA[An algorithm is developed to estimate sea surface salinity (SSS) from the combined use of outgoing longwave radiation and freshwater flux derived by the First Generation Meteosat Visible and InfraRed Imager and Tropical Rainfall Measuring Mission data sets, respectively. A preliminary assessment of the estimated SSS is carried out in the Bay of Bengal during the southwest monsoon season (June&#x2013;September). The monthly estimated SSS at <formula formulatype="inline"><tex Notation="TeX">$1^{circ} times 1^{circ}$</tex></formula> spatial resolution shows a significant correlation ranging from 0.83 to 0.93 and a root-mean-square error of 0.4&#x2013;0.5 psu with the in situ-based objectively analyzed SSS from the Japan Agency for Marine-Earth Science and Technology (JAMSTEC). The independent SSS estimated from the present algorithm would provide supplementary information to verify the spatiotemporal variability of SSS along with the comprehensive SSS maps by the two recent salinity satellite missions.]]></content:encoded>
			<wfw:commentRss>http://www.grss-ieee.org/sea-surface-salinity-estimation-in-the-bay-of-bengal-using-multisatellite-measurements/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Unsupervised Classification of Fully Polarimetric SAR Images Based on Scattering Power Entropy and Copolarized Ratio</title>
		<link>http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6331511</link>
		<comments>http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6331511#comments</comments>
		<pubDate>Wed, 01 May 2013 00:00:00 +0000</pubDate>
		<dc:creator>Geoscience and Remote Sensing Letters, IEEE - new TOC</dc:creator>
				<category><![CDATA[Letters]]></category>
		<category><![CDATA[syndication]]></category>

		<guid isPermaLink="false">http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6331511</guid>
		<description><![CDATA[This letter presents a new unsupervised classification method for polarimetric synthetic aperture radar (POLSAR) images. Its novelties are reflected in three aspects: First, the scattering power entropy and the copolarized ratio are combined to produce...]]></description>
			<content:encoded><![CDATA[This letter presents a new unsupervised classification method for polarimetric synthetic aperture radar (POLSAR) images. Its novelties are reflected in three aspects: First, the scattering power entropy and the copolarized ratio are combined to produce initial segmentation. Second, an improved reduction technique is applied to the initial segmentation to obtain the desired number of categories. Finally, to improve the representation of each category, the data sets are classified by an iterative algorithm based on a complex Wishart density function. By using complementary information from the scattering power entropy and the copolarized ratio, the proposed method can increase the separability of terrains, which can be of benefit to POLSAR image processing. Three real POLSAR images, including the RADARSAT-2 C-band fully POLSAR image of western Xi'an, China, are used in the experiments. Compared with the other three state-of-the-art methods, <formula formulatype="inline"><tex Notation="TeX">$hbox{H}/alpha$</tex> </formula>-Wishart method, Lee category-preserving classification method, and Freeman decomposition combined with the scattering entropy method, the final classification map based on the proposed method shows improvements in the accuracy and efficiency of the classification. Moreover, high adaptability and better connectivity are observed.]]></content:encoded>
			<wfw:commentRss>http://www.grss-ieee.org/unsupervised-classification-of-fully-polarimetric-sar-images-based-on-scattering-power-entropy-and-copolarized-ratio/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Aquarius Third Stokes Parameter Measurements: Initial Results</title>
		<link>http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6298933</link>
		<comments>http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6298933#comments</comments>
		<pubDate>Wed, 01 May 2013 00:00:00 +0000</pubDate>
		<dc:creator>Geoscience and Remote Sensing Letters, IEEE - new TOC</dc:creator>
				<category><![CDATA[Letters]]></category>
		<category><![CDATA[syndication]]></category>

		<guid isPermaLink="false">http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6298933</guid>
		<description><![CDATA[This letter reports a first look at the polarimetric (third Stokes parameter) channel on the Aquarius L-band radiometer that was launched in June of 2011 on the Aquarius/Sat&#xE9;lite de Aplicaciones Cientificas (SAC)-D observatory. The primary purpo...]]></description>
			<content:encoded><![CDATA[This letter reports a first look at the polarimetric (third Stokes parameter) channel on the Aquarius L-band radiometer that was launched in June of 2011 on the Aquarius/Sat&#x00E9;lite de Aplicaciones Cientificas (SAC)-D observatory. The primary purpose of the polarimetric channel is to provide an in situ measure of Faraday rotation which can be important for remote sensing at L-band, particularly in the case of sea surface salinity. However, it also provides an additional mode of observation and a chance to look for new features of the surface. Initial results show good agreement with expectations. In particular, the values of retrieved Faraday rotation agree with predicted values, and a nonzero signal is seen to occur over mixed scenes as predicted by theory.]]></content:encoded>
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		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>A Study of SMOS RFI Over North America</title>
		<link>http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6298931</link>
		<comments>http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6298931#comments</comments>
		<pubDate>Wed, 01 May 2013 00:00:00 +0000</pubDate>
		<dc:creator>Geoscience and Remote Sensing Letters, IEEE - new TOC</dc:creator>
				<category><![CDATA[Letters]]></category>
		<category><![CDATA[syndication]]></category>

		<guid isPermaLink="false">http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6298931</guid>
		<description><![CDATA[The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission has been providing L-band brightness temperature observations of the Earth since its launch in November 2009. Radio frequency interference (RFI) is clearly present in SMOS data...]]></description>
			<content:encoded><![CDATA[The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission has been providing L-band brightness temperature observations of the Earth since its launch in November 2009. Radio frequency interference (RFI) is clearly present in SMOS data, and RFI detection and mitigation are a challenging problem. Furthermore, the interferometric nature of SMOS observations can cause RFI artifacts in SMOS measurements. This letter reports an analysis of the characteristics of SMOS RFI in North America, including a study of RFI artifacts and a method for their removal. Polarimetric properties and statistics of the resulting observations after artifact removal are also examined as an initial step in characterizing the &#x201C;true&#x201D; RFI sources observed in North America.]]></content:encoded>
			<wfw:commentRss>http://www.grss-ieee.org/a-study-of-smos-rfi-over-north-america/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Multirelaxation Generalized Refractive Mixing Dielectric Model of Moist Soils</title>
		<link>http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6332477</link>
		<comments>http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6332477#comments</comments>
		<pubDate>Wed, 01 May 2013 00:00:00 +0000</pubDate>
		<dc:creator>Geoscience and Remote Sensing Letters, IEEE - new TOC</dc:creator>
				<category><![CDATA[Letters]]></category>
		<category><![CDATA[syndication]]></category>

		<guid isPermaLink="false">http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6332477</guid>
		<description><![CDATA[In this letter, a multirelaxation generalized refractive mixing dielectric model (GRMDM) for moist soil is proposed and substantiated in the frequency range from 0.04 to 26.5 GHz. This model is based on the methodology of a single-relaxation GRMDM whic...]]></description>
			<content:encoded><![CDATA[In this letter, a multirelaxation generalized refractive mixing dielectric model (GRMDM) for moist soil is proposed and substantiated in the frequency range from 0.04 to 26.5 GHz. This model is based on the methodology of a single-relaxation GRMDM which accounts only for the dipole relaxation of water molecules in the gigahertz frequency range. The proposed multirelaxation GRMDM takes into account both the dipole (Debye) and ionic (Maxwell&#x2013;Wagner) relaxations of soil water molecules. For this purpose, it uses a two-frequency Debye relaxation equation for the dielectric spectra of bound water. The spectroscopic parameters of the multirelaxation GRMDM were derived by fitting the spectra calculated by this model to the respective measured ones. The main advantage of this model is that it predicts the complex dielectric constant of moist soils throughout the megahertz and gigahertz frequency ranges with the same error as the single-relaxation GRMDM does only in the gigahertz range.]]></content:encoded>
			<wfw:commentRss>http://www.grss-ieee.org/multirelaxation-generalized-refractive-mixing-dielectric-model-of-moist-soils/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Effect of Different MODIS Emissivity Products on Land-Surface Temperature Retrieval From GOES Series</title>
		<link>http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6329409</link>
		<comments>http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6329409#comments</comments>
		<pubDate>Wed, 01 May 2013 00:00:00 +0000</pubDate>
		<dc:creator>Geoscience and Remote Sensing Letters, IEEE - new TOC</dc:creator>
				<category><![CDATA[Letters]]></category>
		<category><![CDATA[syndication]]></category>

		<guid isPermaLink="false">http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6329409</guid>
		<description><![CDATA[The National Oceanic and Atmospheric Administration's National Environmental Satellite, Data, and Information Service is developing an operational land-surface temperature (LST) product from the U.S. Geostationary Operational Environmental Satellite (G...]]></description>
			<content:encoded><![CDATA[The National Oceanic and Atmospheric Administration's National Environmental Satellite, Data, and Information Service is developing an operational land-surface temperature (LST) product from the U.S. Geostationary Operational Environmental Satellite (GOES) series 13, 14, and 15, which makes use of the Moderate Resolution Imaging Spectroradiometer (MODIS) monthly emissivity. However, there is a latency problem since the MODIS monthly emissivity data are available at least a month late. In this study, we investigated using alternative emissivity data sets, including the ten-year monthly average emissivity, the last month emissivity, and the same month emissivity in the last year. We also tested current monthly emissivity and current weekly emissivity for comparison and evaluation. The study area is in the continental United States (<formula formulatype="inline"><tex Notation="TeX">$25^{circ}hbox{N}{-}50^{ circ}hbox{N}$</tex></formula> and <formula formulatype="inline"><tex Notation="TeX">$125^{circ}hbox{W}{-}65^{circ}hbox{W}$ </tex></formula>), and the temporal frame is April, July, October, and December, which represents the four seasons in a year. Based on the modified dual-window algorithm, LST is derived and validated against the SURFace RADiation (SURFRAD) budget network ground observations. The results show that the ten-year monthly average emissivity performs best by retrieving stable and accurate LST.]]></content:encoded>
			<wfw:commentRss>http://www.grss-ieee.org/effect-of-different-modis-emissivity-products-on-land-surface-temperature-retrieval-from-goes-series/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Estimation of the Sea-Surface Slope Variance Based on the Power Spectrum Width of a Radar Scatterometer</title>
		<link>http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6329402</link>
		<comments>http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6329402#comments</comments>
		<pubDate>Wed, 01 May 2013 00:00:00 +0000</pubDate>
		<dc:creator>Geoscience and Remote Sensing Letters, IEEE - new TOC</dc:creator>
				<category><![CDATA[Letters]]></category>
		<category><![CDATA[syndication]]></category>

		<guid isPermaLink="false">http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6329402</guid>
		<description><![CDATA[In this letter, we introduce the possibility of the sea-surface slope measurements using second-order statistics of return waveforms from sea surface in a continuous-wave radar scatterometer. It has been shown that the estimation of the variance of sea...]]></description>
			<content:encoded><![CDATA[In this letter, we introduce the possibility of the sea-surface slope measurements using second-order statistics of return waveforms from sea surface in a continuous-wave radar scatterometer. It has been shown that the estimation of the variance of sea-surface slopes can be obtained by measuring the signal power or the power-spectrum width of return waveforms. We found that the power-spectrum-width-based estimation approach is more sensitive to the variance of the sea-surface slope for weak sea-surface disturbances in contrast to the power-based one.]]></content:encoded>
			<wfw:commentRss>http://www.grss-ieee.org/estimation-of-the-sea-surface-slope-variance-based-on-the-power-spectrum-width-of-a-radar-scatterometer/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Exploiting Sparsity in Hyperspectral Image Classification via Graphical Models</title>
		<link>http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6297997</link>
		<comments>http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6297997#comments</comments>
		<pubDate>Wed, 01 May 2013 00:00:00 +0000</pubDate>
		<dc:creator>Geoscience and Remote Sensing Letters, IEEE - new TOC</dc:creator>
				<category><![CDATA[Letters]]></category>
		<category><![CDATA[syndication]]></category>

		<guid isPermaLink="false">http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6297997</guid>
		<description><![CDATA[A significant recent advance in hyperspectral image (HSI) classification relies on the observation that the spectral signature of a pixel can be represented by a sparse linear combination of training spectra from an overcomplete dictionary. A spatiospe...]]></description>
			<content:encoded><![CDATA[A significant recent advance in hyperspectral image (HSI) classification relies on the observation that the spectral signature of a pixel can be represented by a sparse linear combination of training spectra from an overcomplete dictionary. A spatiospectral notion of sparsity is further captured by developing a joint sparsity model, wherein spectral signatures of pixels in a local spatial neighborhood (of the pixel of interest) are constrained to be represented by a common collection of training spectra, albeit with different weights. A challenging open problem is to effectively capture the class conditional correlations between these multiple sparse representations corresponding to different pixels in the spatial neighborhood. We propose a probabilistic graphical model framework to explicitly mine the conditional dependences between these distinct sparse features. Our graphical models are synthesized using simple tree structures which can be discriminatively learnt (even with limited training samples) for classification. Experiments on benchmark HSI data sets reveal significant improvements over existing approaches in classification rates as well as robustness to choice of training.]]></content:encoded>
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		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Metric Based on Morphological Dilation for the Detection of Spatially Significant Zones</title>
		<link>http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6293849</link>
		<comments>http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6293849#comments</comments>
		<pubDate>Wed, 01 May 2013 00:00:00 +0000</pubDate>
		<dc:creator>Geoscience and Remote Sensing Letters, IEEE - new TOC</dc:creator>
				<category><![CDATA[Letters]]></category>
		<category><![CDATA[syndication]]></category>

		<guid isPermaLink="false">http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6293849</guid>
		<description><![CDATA[The ability to derive spatially significant zones (e.g., water bodies and zones of influence) within a cluster of zones has interesting applications in understanding commonly sharing physical mechanisms. Using a morphological dilation distance techniqu...]]></description>
			<content:encoded><![CDATA[The ability to derive spatially significant zones (e.g., water bodies and zones of influence) within a cluster of zones has interesting applications in understanding commonly sharing physical mechanisms. Using a morphological dilation distance technique, we introduce geometric-based criteria that serve as indicator of the spatial significance of zones within a cluster of zones. This letter focuses on the problem of identifying zones that are &#x201C;strategic&#x201D; in the sense that they are the most central or important based on their proximity to other zones. We have applied this technique to a task aiming at detecting a spatially significant water body from a cluster of water bodies retrieved from Indian Remote Sensing Satellite Linear Imaging Self-scanning Sensor (IRS LISS-III) multispectral satellite data.]]></content:encoded>
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