Remote sensing

Satellites-sensors.html

@@ -225,15 +225,15 @@ <h4>The pixels</h4>
             Each pixel holds a numeric value, known as a pixel value, reflecting attributes like color or brightness. In remote sensing, pixel values encode information about the observed scene, 
             facilitating data interpretation and analysis. These values are fundamental for understanding and processing digital imagery.
 
-                <img src="assets/img/remote-sensing/pixel-values.gif" alt="" style="max-width: 100%; max-height: 100%;">
+                <img src="assets/img/remote-sensing/pixel-values.gif" alt="" style="max-width: 80%; max-height: 80%;">
 
             Human vision perceives color through detecting the entire visible spectrum, and our brains process this information into distinct colors. In contrast, many sensors work by capturing 
             information within narrow wavelength ranges, storing it in channels or bands. Digital representation involves combining and displaying these channels using primary colors 
             (blue, green, and red). Each channel's data is represented by one of these colors, and the relative brightness (digital value) of each pixel in each channel determines the 
             final color by combining the primary colors in varying proportions. <p></p>
 
             <figure style="text-align: center;">
-                <img src="assets/img/remote-sensing/the-pixels-1.png" alt="" style="max-width: 100%; max-height: 100%;">
+                <img src="assets/img/remote-sensing/the-pixels-1.png" alt="" style="max-width: 80%; max-height: 80%;">
                 <figcaption style="text-align: center;"><strong>Image credit:</strong><a href="https://ieeexplore.ieee.org/document/6658949" target="_blank"> © Lefei Zhang et al.</a></figcaption>
             </figure>
 
@@ -285,7 +285,7 @@ <h4>Swaths:</h4>
             frequencies, making them suitable for applications requiring frequent observations. GEO satellites, on the other hand, have a fixed view but cover a larger area with each pass, making 
             them ideal for continuous monitoring.
             <figure style="text-align: center;">
-                <img src="assets/img/remote-sensing/swath.png" alt="" style="max-width: 100%; max-height: 100%;">
+                <img src="assets/img/remote-sensing/swath.png" alt="" style="max-width: 50%; max-height: 50%;">
                 <figcaption style="text-align: center;"><strong>Image credit:</strong><a href="https://natural-resources.canada.ca/maps-tools-and-publications/satellite-imagery-and-air-photos/tutorial-fundamentals-remote-sensing/satellites-and-sensors/satellite-characteristics-orbits-and-swaths/9283" target="_blank"> © Remote Sensing Tutorials.</a></figcaption>
             </figure>
 
@@ -328,7 +328,8 @@ <h4>1. Spatial Resolution, Pixel Size, and Scale</h4>
                     </ul>
                     <img src="assets/img/remote-sensing/IFOV.png" alt="" style="max-width: 50%; max-height: 50%;">
 
-                <li>Images where only large features are visible are said to have coarse or low resolution. In fine or high resolution images, small objects can be detected.</li>
+                <li>Images where only large features are visible are said to have coarse or low resolution. In fine or high resolution images, small objects can be detected.</li><br>
+                <img src="assets/img/remote-sensing/resolution.png" alt="" style="max-width: 50%; max-height: 50%;">
             </ul>
 
             <h4>2. Spectral Resolution </h4> 
@@ -348,7 +349,7 @@ <h4>2. Spectral Resolution </h4>
                     resolution facilitates the accurate identification of these signatures, contributing to more precise classification and interpretation.</li>
             </ul>
 
-            <h4>Temporal resolution</h4>
+            <h4>3. Temporal resolution</h4>
             <p>
                 In remote sensing, temporal resolution refers to the frequency or repeat cycle with which a sensor acquires data over the same location. It is typically expressed in days, weeks, or months, 
                 and it is an important factor in determining the suitability of remote sensing data for a particular application.</p>
@@ -384,6 +385,7 @@ <h2>References</h2>
                     <li><a href="https://www.esa.int/SPECIALS/Eduspace_EN/SEM4HR3Z2OF_0.html" target="_blank">Analogue versus digital images.</a></li>
                     <li><a href="http://www.lmars.whu.edu.cn/prof_web/zhangliangpei/rs/publication/Hyperspectral%20Remote%20Sensing%20Image%20Subpixel.pdf" target="_blank">Hyperspectral Remote Sensing Image Subpixel
                         Target Detection Based on Supervised Metric Learning.</a></li>
+                    <li><a href="https://blog.descarteslabs.com/a-look-into-the-fundamentals-of-remote-sensing" target="_blank">Educational Series (part one): A Look into the Fundamentals of Remote Sensing</a></li>
                 </ul>
             </section>