Abstract
ABSTRACTFor remote-sensing applications such as spectra classification or identification, atmospheric correction constitutes a very important pre-processing step, especially in complex urban environments where a lot of phenomenons alter the shape of the signal. The objective of this article is to compare the efficiency of two atmospheric correction algorithms, COCHISE (atmospheric COrrection Code for Hyperspectral Images of remote-sensing SEnsors) and an empirical method, on hyperspectral data and for classification applications. Classification is carried out on several simulated spaceborne data sets with different spatial resolutions (from 1.6 to 9.6 m). Four classifiers are considered in the study: a k-means, a Support Vector Machine (SVM), and a sun/shadow version of each of them, which processes sunlit and shadowed pixels separately. Results show that the most relevant atmospheric method for classification depends on the spatial resolution of the processed data set. Indeed, if the empirical method performs better on high-resolution data sets (up to 4%), its superiority fades out as the spatial resolution decreases, especially with the lower spatial resolution where COCHISE can be 10% more accurate than the empirical method.
Highlights
During the last century, urban areas grew in such a manner that more than 50% of mankind lives in cities (Chen et al 2013)
The evaluation of the quality of the results produced by the two atmospheric correction algorithms described in this study has been conducted through a sensitivity study involving several parameters: spatial resolution, classification approach and, in the supervised case, training set composition
We compared the efficiency of two atmospheric correction algorithms, the COCHISE method and a classification-orientated empirical method, for the classification of urban hyperspectral data
Summary
Urban areas grew in such a manner that more than 50% of mankind lives in cities (Chen et al 2013) These areas are complex and dynamic ecosystems consuming a huge amount of energy and materials on a daily basis. Numerous sustainable development programmes in needs of an increasing mass of information have emerged. This need can be efficiently fulfilled by the Earth observation technologies such as spatial remote sensors, able to gather quickly and recurrently a large quantity of image data, which are usable as part of many applications: air quality control, ground cartography, material aging monitoring, and vegetal biodiversity characterization. Optical remote sensing has proved to be a powerful tool in order to conduct urban studies
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