Ication, georeferencing, and stacking [41]. Additionally, these sensors are increasingly getting
Ication, georeferencing, and stacking [41]. Also, these sensors are increasingly getting employed in machine learning algorithms for site-specific weed management (SSWM) [40,49,50]. 5.1.3. Hyperspectral Sensor The hyperspectral sensor analyzes a broad spectrum of light, in place of assigning key colors (red, green, and blue). These sensors can record hundreds of narrow radiometric spectral bands from visible to infrared, occasionally up to microwave ranges. Its capacity in giving narrow radiometric spectral bands can detect particular field issues. Thus, customers can compute narrowband indices, for instance the chlorophyll absorption ratio index (CARI), transformed chlorophyll absorption ratio index (TCARI), triangular vegetation index (TVI), and photochemical reflectance index (PRI) [51]. Preparing hyperspectral data is far more complex than RGB and multispectral sensors due to the fact its radiometric and atmospheric calibration workflows are far more complex. Sensor calibration approaches are generated from the UAV’s hyperspectral platforms, which use simulated targets to check information high quality, correct radiance, and supply high-quality reflectance data [52]. For that reason, common methods in acquiring and preparing hyperspectral data captured by UAV remote sensing are: (1) setting up a flight program, (2) image size and data storage, and (three) top quality assessment [41]. Table two summarizes the qualities of every single sensor alongside its benefits and disadvantages.Table two. Characteristic of RGB, multispectral, and hyperspectral sensors. Sensors/Details Resolution (Mpx) Spectral range (nm) Spectral bands Weight (approx.) (kg) Price (approx.) (USD) Benefits RGB 162 40000 3 0.5.five 950780 High-quality images Low-cost operational demands No require for radiometric and atmospheric calibration Multispectral 1.2.two 40000 30 0.18.7 35600,160 Have more than 3 bands Can generates a lot more vegetation indices than RGB Hyperspectral 0.0025.two 300500 4060 0.032 47,4349,293 Numerous narrow radiometric bands Can calculate narrowband indices that may target distinct concerns. Highly-priced, heavier, and much more extensive in comparison to the other sensors Difficult system Complicated radiometric and atmospheric calibration Unable to deliver a high-quality resolution imageDisadvantagesOnly have 3 bands A limited number of vegetation indices can be computedRadiometric and atmospheric calibration is compulsory Unable to provide a high-quality resolution imageAppl. Sci. 2021, 11,8 of5.2. Image Mosaicking and Calibration Images acquired from UAVs can be mosaicked working with a Pix4D mapper (Pix4D, Prilly, Switzerland), Agisoft Photoscan Pro (Agisoft LLC, 52 St. Petersburg, Russia), and any out there industrial application to create qualitative, high-resolution orthomosaic pictures. Soon after mosaicking, the approach will continue with radiometric calibration and rescale the intensity from the electromagnetic radiation or digital quantity (DN) in to the Alvelestat site percentage of reflectance values [53]. Researchers have implemented a lot of solutions, which include the classic empirical line correction approach and contemporary automatic radiometric calibration making use of available industrial application. The empirical line correction approach is UCB-5307 TNF Receptor definitely an atmospheric correction strategy that provides a straightforward surface reflectance calibration system, if a set of invariants inside the time calibration target measurement is supplied. Kelcey and Lucieer [54] implemented this method to enhance six multispectral UAV data top quality bands for qua.