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diff --git a/Data/figs/Figure2.png b/Data/figs/Figure2.png
diff --git a/Data/img/PortugalMeadow_small.png b/Data/img/PortugalMeadow_small.png
diff --git a/docs/Data/figs/Figure2.png b/docs/Data/figs/Figure2.png
diff --git a/docs/Data/img/PortugalMeadow_small.png b/docs/Data/img/PortugalMeadow_small.png
diff --git a/docs/index.html b/docs/index.html
@@ -146,7 +146,7 @@ <h1>DISCOV</h1>
 <p>This paper has produced during Simon Oiry’s PhD and the <a href="https://bicome.info">BiCOME project</a>. The project is one of three studies that form part of the European Space Agency’s ‘Biodiversity+ Precursors’ on&nbsp;<a href="https://www.eo4diversity.info/">Terrestrial (EO4DIVERSITY)</a>,&nbsp;<a href="https://www.biomondo.info/">Freshwater (BIOMONDO)</a>&nbsp;and Coastal ecosystems (BiCOME).</p>
 <p><img src="Data/figs/Micasense_Dual_MX.png" align="left" width="20%" title="Micasense RedEdge-MX Dual"></p>
 <p>This work demonstrates the development and application of a Neural Network classification model trained on a Micasense RedEdge-MX Dual multispectral drone camera. This model is called <strong>DISCOV</strong>, which stands for <strong>D</strong>rone <strong>I</strong>ntertidal <strong>S</strong>ubstrats <strong>C</strong>lassification <strong>O</strong>f <strong>V</strong>egetation.</p>
-<p><img src="Data/figs/Figure2.jpg" width="40%" align="right" title="Classes of the model"></p>
+<p><img src="Data/figs/Figure2.png" width="40%" align="right" title="Classes of the model"></p>
 <p>DISCOV is designed to classify soft bottom sediments, such as mudflats and sandflats, as well as the vegetation typically found in these habitats. The primary objective of this model is to accurately distinguish between seagrasses and green macroalgae. This distinction presents a significant challenge in remote sensing for accurately classifying coastal habitats, owing to the similar pigment compositions of these two types of vegetation. In the image on the right, you can see the spectral signature for each vegetation class identified by the model.</p>
 <section id="input-and-output-of-the-model" class="level2">
 <h2 class="anchored" data-anchor-id="input-and-output-of-the-model">Input and Output of the model</h2>

diff --git a/index.qmd b/index.qmd
@@ -12,7 +12,7 @@ This paper has produced during Simon Oiry's PhD and the [BiCOME project](https:/
 This work demonstrates the development and application of a Neural Network classification model trained on a Micasense RedEdge-MX Dual multispectral drone camera. This model is called **DISCOV**, which stands for **D**rone **I**ntertidal **S**ubstrats **C**lassification **O**f **V**egetation.
 
 
-<img src="Data/figs/Figure2.jpg" width="40%" align="right"
+<img src="Data/figs/Figure2.png" width="40%" align="right"
 title="Classes of the model">
 
 DISCOV is designed to classify soft bottom sediments, such as mudflats and sandflats, as well as the vegetation typically found in these habitats. The primary objective of this model is to accurately distinguish between seagrasses and green macroalgae. This distinction presents a significant challenge in remote sensing for accurately classifying coastal habitats, owing to the similar pigment compositions of these two types of vegetation. In the image on the right, you can see the spectral signature for each vegetation class identified by the model.