Introduction to Hyperspectral

Introduction to Hyperspectral1Hyperspectral DataHyperspectral sensors (sometimes referred to as imaging spectrometers) It acquire image data simultaneously in many, very narrow, contiguous spectral bands ranging through the visible, near-Infra Red (IR), mid-IR, and thermal IR portions of the electromagnetic spectrum.Contains excessive number of bands hundreds of bands2Theory of hyperspectralHyperspectral images are produced by instruments called imaging spectrometers.As applied to the field of optical remote sensing, spectroscopy deals with the spectrum of sunlight that is diffusely reflected (scattered) by materials at the Earths surface.

3Instruments called spectrometers (or spectroradiometers) are used to make ground-based or laboratory measurements of the light reflected from a test material.

The image spectra can be compared with field or laboratory reflectance spectra in order to recognize and map surface materials such as particular types of vegetation species or diagnostic minerals associated with ore deposits.

4Image data from several hundred bands are recorded at the same time offering much greater spectral resolution than a sensor covering wider bands.

5Why Hyperspectral??Since hyperspectral images recorded reflectance from each pixel in many, narrow, contiguous wavelength intervals it provided detailed spectral signatures for every pixel.

It is useful for quantifying different types of building materials or minerals that present at area of interest or even within single pixel.6

Pre-processing of Hyperspectral Data

Atmospheric and Radiometric Correction of Hyperspectral RSGeometric Correction of Hyperspectral DataSpectral Data ReductionSpatial Data Reduction7Main procedures for analyzing hyperspectral and multispectral images (Qin et al., 2013).

8The key for hyperspectral image analysis is to reduce the spectral dimension and extract useful information for qualitative/quantitative analysis.

Hyperspectral data contains a wealth of data, but data extraction process requires an exact understanding of the type of ground surface materials we are trying to measure, and how they relate to the measurements collected by hyperspectral sensors. Some of the recorded data may be found to be redundant and need some processing to reduce the redundancy, either in spatial or spectral data space.

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