The fact that they are discrete and somewhat narrow is what sets multispectral images at the visible wavelength apart from color photography. Test objectives are useful for assessing or calibrating the performance or image quality of an imaging system. In simpler terms, hyperspectral imaging (HSI) is a technique for capturing images that contain information from a wider portion of the electromagnetic spectrum. Companies are increasingly developing their own in-house expertise in aerial imagery and videography as they are cost-effective and highly useful.
Hyperspectral images measure continuous spectral bands, unlike multiband images that measure spaced spectral bands. The method called flat scanning visualizes the entire 2D area at once, but at each wavelength interval, and involves numerous image captures to create the spectral depth of the hyperspectral data cube. Most drone flight planning programs automatically calculate flight lines, image intervals and camera gimbal positions from a polygon or point of interest to facilitate the development of such images. Cameras can capture a lot of images quickly, provide a large field of view and adapt to wide-area image processing applications in agriculture.
Remote sensing, obtaining aerial images of the Earth's surface using unmanned aerial vehicles (UAVs) and satellites, has depended on both HSI and MSI for decades. The acquisition and processing of hyperspectral images is also called image spectroscopy or, with reference to the hyperspectral cube, 3D spectroscopy. Other terms such as remotely piloted vehicle (RPV), unmanned aircraft systems (UAS), unmanned aerial vehicle (UAV), or small unmanned aircraft system (SUAS) are commonly used. With the improvement of image sensors and cameras, researchers and developers are finding more and more applications for obtaining hyperspectral images today, such as food quality control, control of pharmaceutical processes, classification of plastics and biological measurements.
Machine vision sensors generate arrays of grayscale values that result in a 2D image of the object within a viewing area. Hyperspectral imaging devices (HSI) for spatial scanning obtain slit spectra by projecting a fringe of the scene into a slit and scattering the cleft image with a prism or grid. Edmund Optics can help you learn how to specify the correct imaging optics as well as provide you with multiple resources and products to exceed your imaging needs. When it comes to taking hyperspectral images from an aircraft or drone, there are certain techniques and equipment that need to be taken into consideration. To begin with, it is important to understand what hyperspectral imaging is and how it differs from multispectral imaging.
Hyperspectral imaging measures continuous spectral bands while multiband images measure spaced spectral bands. This means that hyperspectral imaging captures more information than multispectral imaging. In order to take hyperspectral images from an aircraft or drone, you will need specialized equipment such as cameras that can capture multiple images quickly with a large field of view. You will also need software that can calculate flight lines, image intervals and camera gimbal positions from a polygon or point of interest in order to create the hyperspectral data cube. Additionally, you will need machine vision sensors that generate arrays of grayscale values which result in a 2D image of the object within a viewing area. Finally, you may want to consider investing in resources from Edmund Optics which can help you learn how to specify the correct imaging optics as well as provide you with multiple products to exceed your imaging needs.
