Abstract
In hyperspectral image classification, so-called spectral endmembers are used as reference data. These endmembers are either extracted from an image or taken from another source. Research has shown that endmembers extracted from an image usually perform best when classifying a single image. However, it is unclear if this also holds when classifying multi-temporal hyperspectral datasets. In this paper, we use spectral angle mapper, which is a frequently used classifier for hyperspectral datasets to classify multi-temporal airborne visible/infrared imaging spectrometer (AVIRIS) hyperspectral imagery. Three classifications are done on each of the images with endmembers being extracted from the corresponding image, and three more classifications are done on the three images while using averaged endmembers. We apply image-to-image registration and change detection to analyze the consistency of the classification results. We show that the consistency of classification accuracy using the averaged endmembers (around 65%) outperforms the classification results generated using endmembers that are extracted from each image separately (around 40%). We conclude that, for multi-temporal datasets, it is better to have an endmember collection that is not directly from the image, but is processed to a representative average.
Highlights
Hyperspectral remote sensing data have been used in many scientific fields for producing thematic maps through a diverse array of classification methods [1,2]
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The use of classification endmembers is a significant part of hyperspectral image processing chain, and endmember selection when dealing with multi-temporal hyperspectral analysis is important for remote sensing studies
Summary
Hyperspectral remote sensing data have been used in many scientific fields for producing thematic maps through a diverse array of classification methods [1,2]. The latter approach has been “mostly preferred and practiced” [3] and has “distinct advantages” [3] These advantages include no influence from different characteristics between diverse sensors, closer to the mineral spectra in the study area than spectra from an existing spectral library and feasible for processing “large quantities of image data” [6,7,8]. These studies concluded that the classification of an image seems best done with endmembers derived from that image, it is unclear what is best for a multi-temporal image collection
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