Hyperspectral Data Processing Algorithms

A. Plaza, J. Plaza, G. Martín, S. Sánchez
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引用次数: 8

Abstract

Hyperspectral imaging is concerned with the measurement, analysis, and interpretation of spectra acquired from a given scene (or specific object) at a short, medium, or long distance by an airborne or satellite sensor [1]. The concept of hyperspectral imaging originated at NASA’s Jet Propulsion Laboratory in California with the development of the Airborne visible infrared imaging spectrometer (AVIRIS), able to cover the wavelength region from 400 to 2500 nm using more than 200 spectral channels, at nominal spectral resolution of 10 nm [2]. As a result, each pixel vector collected by a hyperspectral instrument can be seen as a spectral signature or fingerprint of the underlying materials within the pixel. The special characteristics of hyperspectral data sets pose different processing problems [3], which must be necessarily tackled under specific mathematical formalisms, such as classification, segmentation, image coding, or spectral mixture analysis [4]. These problems also require specific dedicated processing software and hardware platforms. In most studies, techniques are divided into full-pixel and mixed-pixel techniques, where each pixel vector defines a spectral signature or fingerprint that uniquely characterizes the underlying materials at each site in a scene [5]. Mostly based on previous efforts in multispectral imaging, full-pixel techniques assume that each pixel vector measures the response of one single underlying material. Often, however, this is not a realistic assumption. If the spatial resolution of the sensor is not fine enough to separate different pure signature classes at a macroscopic level, these can jointly occupy a single pixel, and the resulting spectral signature will be a composite of the individual pure spectra, called endmembers in hyperspectral terminology [6]. Mixed pixels can also result when distinct materials are combined into a homogeneous or intimate mixture, which occurs independently of the spatial resolution of the sensor. contents
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高光谱数据处理算法
高光谱成像是通过机载或卫星传感器对给定场景(或特定物体)在近、中、远距离获取的光谱进行测量、分析和解释[1]。高光谱成像的概念起源于美国宇航局位于加州的喷气推进实验室,随着机载可见红外成像光谱仪(AVIRIS)的发展,该光谱仪能够覆盖400至2500 nm的波长区域,使用200多个光谱通道,标称光谱分辨率为10 nm[2]。因此,由高光谱仪器收集的每个像素矢量可以被视为像素内底层材料的光谱签名或指纹。高光谱数据集的特殊特性带来了不同的处理问题[3],必须在特定的数学形式下进行处理,如分类、分割、图像编码或光谱混合分析[4]。这些问题还需要专门的软件和硬件平台来处理。在大多数研究中,技术分为全像素和混合像素技术,其中每个像素向量定义了一个光谱特征或指纹,该光谱特征或指纹独特地表征了场景中每个站点的底层材料[5]。基于先前在多光谱成像方面的努力,全像素技术假设每个像素向量测量一个单一底层材料的响应。然而,这通常不是一个现实的假设。如果传感器的空间分辨率不够精细,无法在宏观层面上分离不同的纯特征类,则这些特征类可以共同占用单个像元,所得到的光谱特征将是单个纯光谱的复合,在高光谱术语中称为端元[6]。当不同的材料组合成均匀或亲密的混合物时,也会产生混合像素,这与传感器的空间分辨率无关。内容
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