The acquired geodata were comprised of multiple 4-band images and point cloud – LAS (with same geographic relation) datasets. The images were then mosaiced to form an orthomosaic raster image. Prior to the data analysis, radiometric corrections were done to correct for the measured brightness value of pixels, band to band error, and geometric and panoramic distortions that occurred during the acquisition process.
To enable for photointerpretation, the orthomosaic raster image was processed. To allow for identification of different features on the digital image, an image classification algorithm was done. Both the supervised and unsupervised classification were performed on the orthomosaic image , hence identifying damaged corn areas and estimating the unused areas on the field. Using a fully trained algorithm, the individual pixels of the orthomosaic were classified with respect to their spectral properties.
The LAS dataset was used to generate geospatial products from the raster image e.g. extraction of the Digital Elevation Model (DEM) and Digital Surface Model (DSM). These are variables needed for band arithmetic in deriving corn heights. These are variables needed for band arithmetic in deriving corn heights. For estimation of corn growth stage, precision and accuracy in photointerpretation of the cornfield, an estimation of the NDVI (Normalized Difference Vegetation Index) was also done on the raster images.
