The global hyperspectral imaging systems market is projected to reach USD 21.33 billion by 2023 from USD 9.51 billion in 2018, at a CAGR of 17.5%. The growing industrial applications of hyperspectral imaging and increasing funding & investments are the major factors driving the growth of the global hyperspectral imaging systems market.
Data storage and high costs associated with the use of hyperspectral imaging
Hyperspectral imaging is a complex process which involves a huge amount of data and thus requires large storage space and enhanced software technologies to analyze the data in a shorter time frame. Although hyperspectral imaging has immense potential for various applications, the high cost of the equipment and manufacturing processes and the requirement of large volumes of data for acquisition and processing are the major factors limiting its adoption for real-time commercial purposes.
Fast computers, sensitive detectors, and large data storage capacities are required for analyzing hyperspectral data. A significant data storage capacity is necessary since hyperspectral cubes are large multi-dimensional datasets, potentially exceeding hundreds of megabytes. These factors greatly increase the cost of acquiring and processing hyperspectral data.
Untapped market opportunities in emerging countries
The emerging economies in Asia and Latin America are expected to offer significant growth opportunities for players in the hyperspectral imaging systems market. In China, for instance, a number of hyperspectral sensors were designed and launched to meet the rising demand for precise and fast mapping of natural resources using remote sensing applications. India has also opened up opportunities for collaborations with other countries. For instance, Bharat Rohan Airborne Innovations is aimed to promote scientific research in India for various applications of hyperspectral imaging & remote sensing. Bharat Rohan, in partnership with BaySpec, offers hyperspectral/multispectral imagers which are ultra-compact, easily deployable, and high-performance systems.
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Attaining super-resolution in a cost-effective manner
Hyperspectral imaging is an emerging technique used by researchers for broad application areas, such as aerial and space imagery, agriculture, ecology, geology, medicine, and industrial inspection. Although the resolution of a hyperspectral image is high in the spectral domain, it has limited resolution in the spatial domain. Moreover, hyperspectral image quality is affected by various factors, such as atmospheric scattering, secondary illumination, and sensor noise. Therefore, it is essential to improve the spatial resolution for better utilization of this technique for various applications. Since changes in hardware (hyperspectral camera units) prove to be expensive, bringing in improvements in spatial resolution through software is a major challenge currently being faced by market players.