Multi-Modal Remote Sensing - Technologies
An overview of the key technologies. Current and ongoing topics include:
Direct georeferencing and system calibration
RGB, LiDAR, and hyperspectral data processing techniques
Quality control strategies
Overview
Recent advancements in remote sensing technologies have significantly expanded their applications, spanning precision agriculture, digital forestry, environmental monitoring, and infrastructure inspection. Mobile mapping systems, such as unmanned aerial vehicles (UAVs), manned aircraft, and ground vehicles, emerge as pivotal tools driving progress and innovation. Such systems can carry a variety of sensors including multispectral/hyperspectral scanners, red-green-blue (RGB) cameras, and LiDAR, and maneuver in the field to acquire high-quality data. Thanks to their rapid survey capabilities and straightforward data acquisition procedures, conducting repeated surveys becomes convenient. By leveraging direct georeferencing techniques and implementing robust system calibration, data captured by different sensors at various times can be accurately aligned to a common reference frame. No external ground control points are required.
The strength of modern mobile mapping systems lies in their ability to perform multi-modal, multi-temporal remote sensing with precision and efficiency (see Figure 1 for sample data). These systems can simultaneously acquire data from all onboard sensors. Multispectral/hyperspectral scanners acquire full spectral information for each pixel. RGB cameras, on the other hand, achieve high spatial resolution and capture intricate geometric features of objects. While imaging sensors only provide planimetric information, LiDAR maps the 3D topography of the landscape, offering vertical information. The complementary nature of data from different sensors underscores the potential of data fusion techniques for obtaining a comprehensive description of the area of interest. In addition, the ease of conducting repeated surveys facilitates the collection of multi-temporal datasets, which is critical for tracking changes over time.
Direct Georeferencing and system calibration are the key technologies to achieve multi-modal, multi-temporal remote sensing. Direct georeferencing involves obtaining the platform's trajectory (specifically, the position and orientation of the UAV) through the onboard global navigation satellite system/inertial navigation system (GNSS/INS) unit. System calibration determines the relative position and orientation parameters between the onboard sensors (e.g., multispectral/hyperspectral scanners, LiDAR, and camera) and the GNSS/INS unit. Leveraging these technologies, data captured by different sensors at varying times can be georeferenced to a common reference frame. In simpler terms, datasets from different sensors, systems, or dates seamlessly align with one another.
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