Global Hyperspectral Imaging Market - Segmented by Application (Surveillance, Remote Sensing, Machine Vision/Optical, Medical Diagnostics), End-user (Food and Agriculture, Healthcare, Defense, Mining, Metrology), and Region - Growth, Trends and Forecast (2018 - 2023)
The global hyperspectral imaging market was valued at USD 61.9 million in 2017, and is expected to reach a value of USD 108.5 million by 2023, at a CAGR of 9.79%, during the forecast period (2018 - 2023). The scope of the report is limited to products offered by major players for various purposes, which include Surveillance, Remote Sensing, Machine Vision/Optical, and Medical Diagnostics. While the end-users considered in the scope of the report include Food & Agriculture, Healthcare, Defense, Mining, and Meteorology.
Hyperspectral imaging is a powerful technology that adds a new dimension to optical imaging. The adoption of hyperspectral imaging is still at an early stage, but it has found traction in the agriculture, chemical analysis, and defense sectors. Hyperspectral imaging technology is widely used in a diverse range of applications. One major application is food surface inspection, in which reflectance imaging is used on various products to check for bruises and other damages. Similarly, it is widely used in military surveillance to monitor potential targets and avoid major calamities.
Technological advancements, such as the development of micro-hyperspectral imaging technology, have led to improvements in data acquisition and analysis. Micro-hyperspectral technology addresses the need for small, lightweight, and efficient hyperspectral imaging instruments that are capable of being deployed in harsh environments. Additionally, there has been an increasing demand for data accuracy and consistency among end-users and the government, as the hyperspectral imaging technology is highly accurate.
Increasing Demand for Data Accuracy and Consistency Have Led to the Growth of Hyperspectral Imaging
In environmental studies, the fusion of HSI sensor and Airborne LiDAR Scanner (ALS) data provides significant potential for applications. In these applications, standard fusion approaches use information obtained from HSI and distance measurements, gathered from the ALS, to increase data dimensionality and accuracy. The cross-calibration approach is followed for the rigorous illumination correction of HSI data, based on the radiometric cross-calibrated return intensity information of ALS data. Further, this method is capable of compensating for the drawbacks of passive HSI systems, such as illumination changes over time, across track illumination, cast and cloud shadowing effects, and partly, anisotropy effects.
PCA will be used for both feature extraction and dimensionality reduction, making it a common technique to be used in HSI analysis, and to make the decision based on captured data ANN’s and SVM methods will be utilized. It is also further used to diagnose diseases in the areas of ophthalmology, oncology, and cardiology, as it accurately depicts molecular activity in the body. It is a non-invasive test that helps in the early diagnosis and treatment of retinal diseases, oral cancer, and disorders of the central nervous system. Doctors use HSI devices to gauge a patient's responses to a therapy and determine the best course of treatment.
Remote Sensing is Expected to be one of the Most Widely Used Applications for Hyperspectral Imaging
The recent advancements in remote sensing and geographic information have offered new opportunities for the development of hyperspectral sensors. Hyperspectral imaging is currently being investigated by researchers and scientists, for the detection and identification of vegetation, minerals, different objects, and backgrounds. The primary applications of hyperspectral remote sensing comprise of agriculture and forestry, geology and mineral exploration, ecology, and coastal zone management.
Furthermore, hyperspectral sensors have revolutionized IR remote sensing systems, significantly improving spectral performance. Different from multispectral sensors that capture data in distinct spectral bands, hyperspectral sensors can capture a near-continuous spectral range, by collecting data in many very narrow spectral bands (typically 10 to 20 nm) over a broad, continuous range, such as from the visible to 2.5 μm. As a result, the quantity of data captured is significantly larger, than that of multispectral systems
North America had the Largest Market Share, with the US Emerging as the Major Contributor
The medical device companies in the United States hold high regard across the globe, for their innovations, superior technology, and high investments in R&D. According to the Department of Commerce (DOC), the US medical device market was valued at USD 148 billion in 2015, accounting for 43% of the global market. Further, the emphasis by the US government on healthcare and special initiatives, like ‘Patient Protection and Affordable Care Act’ have aided the growth of the medical device market. The food industry accounts for around 5% of the country’s GDP, and is growing owing to a greater demand for packaged food. The growing availability of packaged dairy products through various retailing channels, along with increasing consumer preference towards protein-based products, such as yogurt, cheese, and butter are expected to drive the adoption of dairy-based packaging in the United States. Owing to the growth of such industries in the region there has been an increased use of hyperspectral imaging in the country.
Key Developments In The Market