<div dir="ltr"><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif">Call for papers</p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif">Special Issue "Machine Learning for Remote Sensing Image/Signal Processing"</p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif">Remote Sensing (MDPI) Journal</p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif"> Submission deadline: 31st December, 2021.</p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif"><br></p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif">Dear Colleagues,</p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif"> </p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif">Machine learning techniques have been applied in remote sensing for more than 20 years now. We are, however, experiencing an explosion of new capabilities and application areas where machine learning in remote sensing is playing and will continue to play a capital role. In particular, new sensors with ever increasing capabilities and new computing hardware and software capabilities are allowing us to tackle problems that were considerably difficult to approach just a few years ago.</p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif"> </p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif">This Special Issue is aimed at presenting new machine learning techniques and new application areas in remote sensing. We particularly welcome papers focused on, although not limited to, one or more of the following topics:</p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif"> </p><p style="margin:0cm 0cm 0.0001pt 36pt;text-align:justify;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif"><span style="font-family:Symbol">·<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">         </span></span>Deep learning techniques for remote sensing</p><p style="margin:0cm 0cm 0.0001pt 36pt;text-align:justify;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif"><span style="font-family:Symbol">·<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">         </span></span>Machine learning techniques for inference and retrieval of bio–geo–physical variables</p><p style="margin:0cm 0cm 0.0001pt 36pt;text-align:justify;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif"><span style="font-family:Symbol">·<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">         </span></span>Machine learning for remote sensing data classification and regression</p><p style="margin:0cm 0cm 0.0001pt 36pt;text-align:justify;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif"><span style="font-family:Symbol">·<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">         </span></span>Multi-temporal and multi-sensor data fusion, assimilation, and processing</p><p style="margin:0cm 0cm 0.0001pt 36pt;text-align:justify;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif"><span style="font-family:Symbol">·<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span>Machine learning platforms for big data and highly demanding remote sensing applications</p><p style="margin:0cm 0cm 0.0001pt 36pt;text-align:justify;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif"><span style="font-family:Symbol">·<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">    </span></span>Machine learning for multispectral and hyperspectral remote sensing platforms and applications</p><p style="margin:0cm 0cm 0.0001pt 36pt;text-align:justify;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif"><span style="font-family:Symbol">·<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">         </span></span>Machine learning for uncertainty analysis and assessment in remote sensing</p><p style="margin:0cm 0cm 0.0001pt 36pt;text-align:justify;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif"><span style="font-family:Symbol">·<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">         </span></span>Machine learning for remote sensing estimation and characterization of highly variable and dynamic earth processes</p><p style="margin:0cm 0cm 0.0001pt 36pt;text-align:justify;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif"> </p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif">We would like this Special Issue to become an example of the most up-to-date machine learning approaches used to solve some of the problems considered by the remote sensing community.</p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif"> </p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif">The website of the Special Issue can be found at:</p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif"> </p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif"><a href="https://www.mdpi.com/journal/remotesensing/special_issues/ML_RS_Processing" target="_blank" style="color:rgb(5,99,193)">https://www.mdpi.com/journal/remotesensing/special_issues/ML_RS_Processing</a></p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif"> </p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif">Prof. Dr. Pedro Latorre-Carmona</p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal;font-size:11pt;font-family:Calibri,sans-serif">Prof. Dr. Antonio J. Plaza</p><span style="font-size:11pt;line-height:15.6933px;font-family:Calibri,sans-serif">Guest Editors</span><br></div>