Savannah Du, 2018, Operations Research and Financial Engineering
This summer I worked with RGB and near-infrared imagery collected by unmanned aerial vehicles (UAVs). The use of UAVs allows capture of high spatial and temporal resolution images, which can be used to monitor crop stress (in our case, fungi patches in southern New Jersey cranberry bogs). However, collected imagery is affected by particle interactions in the atmosphere. This atmospheric attenuation, in addition to other issues such as uneven illumination and changing camera angle, must be corrected when comparing images over time and different areas. I worked on devising a method to normalize illumination within and between images and to calibrate them to ground reflectance such that they would most accurately represent surface characteristics. Through this experience, I was able to get a glimpse of some of the important issues and progress in remote sensing while working on implementing my understanding through code. Although my work was not directly related to my academic study, this internship taught me what the research process entails and how to approach issues and failures from different angles, which will undoubtedly be helpful for future endeavors.