Shearlet-Based Structure-Aware Filtering for Hyperspectral and LiDAR Data Classification

<div class="floats-full-page floats-full-page-figure"><h2>Figure 13</h2><div class="floats-full-page-caption"><div>Overall accuracy of various spatial filters as functions of the number of labeled samples per class on the (a) Houston, (b) Trento, and (c) MUUFL Gulfport datasets.</div></div><div class="floats-full-page-content"><table class="figure-group"><tr class="fig-image"><td><object data="https://d31iiq90dwlpfy.cloudfront.net/articles/remotesensing/volume-2021/9825415/figures/9825415.fig.0013.svgz" name="9825415.fig.0013" type="image/svg+xml"></object></td></tr></table></div></div>

Figure 13 | Shearlet-Based Structure-Aware Filtering for Hyperspectral and LiDAR Data Classification

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Figure 13: Shearlet-Based Structure-Aware Filtering for Hyperspectral and LiDAR Data Classification

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Figure 13

About this journal

<p>The <em>Journal of Remote Sensing</em>, an Open Access journal published in association with AIR-CAS, promotes the theory, science, and technology of remote sensing, as well as interdisciplinary research within earth and information science.</p>


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<p>The <em>Journal of Remote Sensing’s</em> editorial board is led by Yirong Wu (Aerospace Information Research Institute, Chinese Academy of Sciences)&nbsp;and is comprised of experts who have made significant and well recognized contributions to the field.</p>


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<p><em>Journal of Remote Sensing</em> is now accepting submissions for its first two special issues:</p>

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