Hyperspectral imaging is a discipline that uses specialized sensors to capture data at numerous narrow wavelengths simultaneously. Hyperspectral data is typically represented as an image cube in which each image represents one of the tens or hundreds of narrow wavelength ranges or spectral bands. Hyperspectral imaging enables measurement and analysis of the spectral characteristics of regions or objects, which are used in applications like agricultural crop health assessment, environmental degradation monitoring, and tissue pathology.
A set of hyperspectral images. Each image was captured at numerous narrow wavelength ranges.
Where Is Hyperspectral Imaging Used?
高光谱成像用于遥感，监控，机器视觉和医学成像等应用，具有识别材料和检测物体，异常和过程的主要目的。例如，遥感中的高光谱成像涉及使用卫星或空中图像检测和识别矿物质，地面植被和人造结构。
使用基于卫星的高光谱数据来识别不同的地面区域，基于它们的材料类型使用最大数量分类.
In medical imaging, hyperspectral imaging analysis of tissue surfaces can provide insightful diagnostic information for tissue pathology.
高光谱图像来自The University of Nottingham，通过在组织表面上闪光，并准确测量氧气水平并产生氧饱和度图以促进临床研究人员和医生的工作。
How Does Hyperspectral Image Processing Work?
高光谱成像开始使用高光谱图像传感器捕获一组图像并表示为三维高光谱数据立方体。This data cube can then be analyzed and processed using a variety of image processing algorithms, mainly for workflows involving classification, spectral matching, and unmixing.
A typical hyperspectral image processing workflow, which involves representing, analyzing, and interpreting information contained in the hyperspectral images.
例如，常见的超光图像处理技术，最大丰度分类（Mac）, is used to classify pixels in an image based on their spectral signatures, and associate them to an object or region class, also known as endmember class.
An abundance map generated using maximum abundance classification of a hyperspectral image with nine endmember classes: Asphalt, Meadows, Gravel, Trees, Painted metal sheets, Bare soil, Bitumen, Self-blocking bricks, and Shadows.
Another common task involves using theNormalized Difference Vegetation Index或植被区域的高光谱图像的NDVI值，并在该值上施加阈值以测量该区域植被的相对健康。NDVI值越高，植被在该地区的更健康和密集。
The (right-most) image with highest NDVI value has indicated regions with healthier and denser vegetation in green.
用matlab高光谱成像
The高光谱成像库in MATLAB®提供一组功能和可视化功能，用于访问，预处理，分析和处理高光谱数据。
TheHyperspectral Viewer appenables visualization and interactive exploration of the hyperspectral data. You can view the individual bands of the hyperspectral data as gray-scale images as well as the color composite representations of the data. The app also enables creating spectral profiles that enable identifying the constituting elements in the hyperspectral data.
Hyperspectral Viewer app for visualizing hyperspectral data and spectral profiles.
The Hyperspectral Imaging Library contains built-in functions for preprocessing, analyzing and interpreting hyperspectral data. It supports workflows fordimensionality reduction,光谱解密,光谱匹配, andclassificationwith techniques such asband selection,终点提取,abundance map estimation,NDVI., and异常检测.
圆润解密的终点提取和丰富地图估计。
MATLAB provides tools for data exploration, visualization, and algorithm development that support hyperspectral imaging workflows. In addition,Image Processing Toolbox™provides a comprehensive set of reference-standard algorithms that you can use with hyperspectral imaging data.
MATLAB用于绘制和可视化光谱匹配分数图。
