Hyperspectral image analysis is a field of study that involves the acquisition, processing, and analysis of images obtained from a hyperspectral sensor. A hyperspectral sensor captures information about an object or scene in many narrow, contiguous wavelength bands, which is then used to create a hyperspectral image. This image can provide detailed information about the composition, structure, and properties of the object or scene being imaged, as well as information about changes that may have occurred over time.
In hyperspectral image analysis, various techniques and algorithms are applied to extract meaningful information from the hyperspectral data. These may include feature extraction, dimensionality reduction, classification, unmixing, and other data analysis methods. The resulting information can be used for a wide range of applications, including remote sensing, mineral exploration, agriculture, environmental monitoring, and more.
In remote sensing, for example, hyperspectral image analysis can be used to identify and map the distribution of different types of vegetation, land cover, and other features on the Earth's surface. In mineral exploration, it can be used to detect minerals and mineral deposits, while in agriculture, it can be used to monitor crop health and detect stress factors that may affect crop yield. In environmental monitoring, it can be used to monitor changes in the environment over time, such as the effects of land use change or climate change.