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<p style="margin-left:0px; margin-right:0px; text-align:left"><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">Hyper-spectral imagery combined with spatial features holds promise for improved remote sensing</span><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">&nbsp;</span></p>
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<p style="margin-left:0px; margin-right:0px; text-align:left"><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">classification. In this letter, we propose a method for classification of hyper-spectral data based on the</span><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">&nbsp;</span></p>
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<p style="margin-left:0px; margin-right:0px; text-align:left"><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">in corporation of spatial arrangement of pixel&rsquo;s values. We use the semi-variogram to measure the spatial correlation which is then combined with spectral features within the stacked kernel support</span><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">&nbsp;</span></p>
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<p style="margin-left:0px; margin-right:0px; text-align:left"><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">vector machine framework. The proposed method is compared with a classifier based on firstorder</span><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">&nbsp;</span></p>
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<p style="margin-left:0px; margin-right:0px; text-align:left"><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">statistics. The overall classification accuracy is tested for the AVIRIS Indian Pines benchmark dataset.</span><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">&nbsp;</span></p>
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<p style="margin-left:0px; margin-right:0px; text-align:left"><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">Error matrices are used to estimate individual class accuracy. Statistical significance of the accuracy</span><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">&nbsp;</span></p>
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<p style="margin-left:0px; margin-right:0px; text-align:left"><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">estimates is assessed based on the kappa coefficient and z-statistics at the 95% confidence level.</span><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">&nbsp;</span></p>
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<p style="margin-left:0px; margin-right:0px; text-align:left"><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">Empirical results show that the proposed approach gives better performance than the method based on</span><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">&nbsp;</span></p>
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<p style="margin-left:0px; margin-right:0px; text-align:left"><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">first-order statistics.</span><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">&nbsp;</span></p>
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<p style="margin-left:0px; margin-right:0px; text-align:left"><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">Abstrat de l&rsquo;article : Hyperspectral data classification using gesostatistics and support vecor machines.</span><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">&nbsp;</span></p>
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<p style="margin-left:0px; margin-right:0px; text-align:left"><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">Publi&eacute; in Remote Sensing letters, Taylor and Francis</span><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">&nbsp;</span></p>
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<p style="margin-left:0px; margin-right:0px; text-align:left"><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">Voil&agrave; un extrait de mon article scientifique ayant pour sujet la classification des donn&eacute;es massives</span><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">&nbsp;</span></p>
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<p style="margin-left:0px; margin-right:0px; text-align:left"><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">(BIG DATA) en se basant sur les g&eacute;ostatistiques et les machines &agrave; vecteurs supports. C&rsquo;est une preuve</span><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">&nbsp;</span></p>
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<p style="margin-left:0px; margin-right:0px; text-align:left"><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">que le Mast&egrave;re DATA SCIENCE &amp;amp; BIG DATA est accessible pour tous les dipl&ocirc;m&eacute;s des branches</span><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">&nbsp;</span></p>
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<p style="margin-left:0px; margin-right:0px; text-align:left"><span style="font-family:Times New Roman,&quot;Times New Roman_EmbeddedFont&quot;,&quot;Times New Roman_MSFontService&quot;,serif; font-size:12pt">scientifiques et non seulement l&rsquo;informatique.</span></p>
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<p>يعد التصوير الطيفي المفرط جنبًا إلى جنب مع الميزات المكانية بتحسين تصنيف الاستشعار عن بعد. الاستشعار عن بعد. في هذه الرسالة، نقترح طريقة تصنيف البيانات فائقة الطيف بناءً على الترتيب المكاني لقيم البكسل. نستخدم نصف فارو جرام لقياس</p>

<p>L&#39;imagerie hyper-spectrale combin&eacute;e &agrave; des caract&eacute;ristiques spatiales est prometteuse pour am&eacute;liorer la classification de la t&eacute;l&eacute;d&eacute;tection.</p>

<p>t&eacute;l&eacute;d&eacute;tection. Dans cette lettre, nous proposons une m&eacute;thode de classification des donn&eacute;es hyper-spectrales bas&eacute;e sur l&#39;arrangement spatial des valeurs des pixels.</p>

<p>Nous utilisons le semi-variogramme pour mesurer l&#39;arrangement spatial des valeurs des pixels. Nous utilisons le semi-variogramme pour mesurer la corr&eacute;lation spatiale qui est ensuite combin&eacute;e avec les caract&eacute;ristiques spectrales dans le cadre de la machine &agrave; vecteur de support &agrave; noyau empil&eacute;.</p>

<p>de support &agrave; noyau empil&eacute;. La m&eacute;thode propos&eacute;e est compar&eacute;e &agrave; un classificateur bas&eacute; sur des statistiques de premier ordre.</p>

<p>statistiques de premier ordre. La pr&eacute;cision globale de la classification est test&eacute;e pour l&#39;ensemble de donn&eacute;es de r&eacute;f&eacute;rence AVIRIS Indian Pines.</p>

<p>Les matrices d&#39;erreur sont utilis&eacute;es pour estimer la pr&eacute;cision de chaque classe. La signification statistique des estimations de</p>

<p>est &eacute;valu&eacute;e sur la base du coefficient kappa et de la statistique z au niveau de confiance de 95 %.</p>

<p>Les r&eacute;sultats empiriques montrent que l&#39;approche propos&eacute;e est plus performante que la m&eacute;thode bas&eacute;e sur les statistiques de premier ordre.</p>

<p>statistiques de premier ordre.</p>

<p>Abstrat de l&#39;article : Hyperspectral data classification using gesostatistics and support vecor machines.</p>

<p>Publi&eacute; in Remote Sensing letters, Taylor and Francis</p>

<p>Voil&agrave; un extrait de mon article scientifique ayant pour sujet la classification des donn&eacute;es massives</p>

<p>(BIG DATA) en se basant sur les g&eacute;ostatistiques et les machines &agrave; vecteurs supports. C&#39;est une preuve</p>

<p>que le Mast&egrave;re DATA SCIENCE &amp;amp ; BIG DATA est accessible pour tous les dipl&ocirc;m&eacute;s des branches</p>

<p>scientifiques et non seulement l&#39;informatique.</p>

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