Abstract
Imaging through hyperspectral technology is a powerful tool that can be used to spectrally identify and spatially map materials based on their specific absorption characteristics in electromagnetic spectrum. A robust method called Tetracorder has shown its effectiveness at material identification and mapping, using a set of algorithms within an expert system decision-making framework. In this study, using some stages of Tetracorder, a technique called classification by diagnosing all absorption features (CDAF) is introduced. This technique enables one to assign a class to the most abundant mineral in each pixel with high accuracy. The technique is based on the derivation of information from reflectance spectra of the image. This can be done through extraction of spectral absorption features of any minerals from their respected laboratory-measured reflectance spectra, and comparing it with those extracted from the pixels in the image. The CDAF technique has been executed on the AVIRIS image where the results show an overall accuracy of better than 96%.
Highlights
The image classifications are based entirely on the spectral signatures of the land cover types
Usually the statistical methods [1] and training samples are being used, whereas the unsupervised classification is based on the comparison between spectral signatures of a pixel and those of different materials collected in spectral libraries [2]
The spectral library of the minerals can be found in the USGS website http://speclab.cr.usgs.gov/spectrallib.html. 4.1
Summary
The image classifications are based entirely on the spectral signatures of the land cover types This area of specialty has attracted the attention of remote sensing researchers in recent years and as a result, the techniques of classification have been improved considerably. Spectral characteristics is a tool that has been used for decades to identify, understand, and quantify solid, liquid, or gaseous materials, especially in the laboratory. This is usually done through detection of absorption features due to the presence of specific chemical bonds, where its depth of absorption represents the abundance and physical state of the detected absorbing species [3,4,5]. Imaging spectroradiometer can acquire data with suitable spectral range, resolution, and sampling rate at every pixel in a raster image, so that individual absorption features can be identified and spatially mapped [6]
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