高光谱成像技术是一门学科,它使用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.
一组高光谱图像。每个图像在许多狭窄的波长范围内捕获。
高光谱成像在哪里使用？
Hyperspectral imaging is used in applications like remote sensing, surveillance, machine vision, and medical imaging with the primary purpose of identifying materials and detecting objects, anomalies, and processes. For example, hyperspectral imaging in remote sensing involves detecting and identifying minerals, terrestrial vegetation, and man-made structures using satellite or aerial images.
Using satellite-based hyperspectral data to identify different terrestrial regions, based on their material type, using最大丰度分类。
在医学成像中，组织表面的高光谱成像分析可以为组织病理提供深刻的诊断信息。
Hyperspectral images from诺丁汉大学，，，，derived by shining light on the tissue surface and accurately measuring oxygen levels and generating oxygen saturation maps to facilitate the work of clinical researchers and doctors.
高光谱图像处理如何工作？
Hyperspectral imaging begins with capturing a set of images using hyperspectral image sensors and representing them as a three-dimensionalhyperspectral data cube.然后，可以使用各种图像处理算法分析和处理此数据立方体，主要用于涉及分类，光谱匹配和毫电混合的工作流程。
典型的高光谱图像处理工作流程，涉及在高光谱图像中代表，分析和解释信息。
For example, a common hyperspectral image processing technique,最大丰度分类(MAC)，用于根据图像的光谱签名将像素分类，并将它们与对象或区域类（也称为endmember类）相关联。
使用九个末日的高光谱图像对高光谱图像的最大丰度分类生成的丰度图：沥青，草地，砾石，树木，彩绘金属板，裸露的土壤，沥青，沥青，自块砖块和阴影。
另一个常见的任务涉及使用归一化差异植被指数or NDVI values of hyperspectral image of a vegetation region and applying a threshold on this value to measure the relative health of vegetation across this region. The higher the NDVI value, the healthier and denser the vegetation is in that region.
NDVI值最高的（右）图像表明，绿色的植被更健康和更密集。
高光谱成像with MATLAB
这高光谱成像Library在Matlab®provides a set of functions and visualization capabilities for accessing, preprocessing, analyzing, and processing hyperspectral data.
这高光谱查看器应用程序实现高光谱数据的可视化和交互式探索。您可以将高光谱数据的单个频段视为灰度图像以及数据的颜色复合表示。该应用程序还启用了创建光谱配置文件，以识别高光谱数据中的构成元素。
高光谱查看器应用程序可视化高光谱数据和光谱曲线。
高光谱成像库包含用于预处理，分析和解释高光谱数据的内置功能。它支持金宝app工作流减少维度，，，，spectral unmixing，，，，spectral matching， 和分类使用诸如乐队选择，，，，endmember extraction，，，，丰度图估计，，，，NDVI， 和anomaly detection。
Endmember extraction and abundance map estimation for spectral unmixing.
MATLAB提供了支持高光谱成像工作流的数据探索，可视化和算法开发的工具。金宝app此外，图像处理工具箱™提供一组全面的参考标准算法，您可以与高光谱成像数据一起使用。
MATLABused for plotting and visualizing the spectral match score maps.
示例以及如何
软件参考
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美洲
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- 美国（英语）