Weighted spectral correlation angle target detection method for land-based hyperspectral imaging

Abstract

In land-based spectral imaging, the spectra of ground objects are inevitably affected by the imaging conditions (weather conditions, atmospheric conditions, light conditions, zenith and azimuth angle conditions) and spatial distribution of targets, leading to uncertainties featured by “same object different spectrum”. That is, the spectrum of a ground object may change within a certain range under different imaging conditions. Traditional target detection (TD) methods are mainly based on similarity measurements and do not fully account for the spectral uncertainties. These detection methods are prone to false detections or missed detections. Therefore, reducing the impact of spectral uncertainties on TD is an important research topic in hyperspectral imaging. In this paper, we first review traditional TD methods and compare their principles and characteristics. It is found that the spectral correlation angle (SCA) method has good adaptability in land-based imaging. The shortcoming of the SCA method that it cannot reflect the local spectrum characteristics, is also analyzed. As the effect of spectral uncertainties cannot be completely overcome by the SCA method, a new similarity measurement method, the weighted spectral correlation angle (WSCA) method, is proposed. It can reduce the influence of spectral uncertainties on TD by increasing the weight of particular bands. Finally, we use two sets of experiments to analyze the effect of the WSCA method on TD. Its performance in overcoming spectral uncertainties caused by variations in imaging conditions or uneven spatial distributions of targets is tested. The results show that the WSCA method can effectively reduce the influence of spectral uncertainties and obtain a good detection result.

Graphical abstract

Keywords

Hyperspectral image / Spectral uncertain feature / Target detection / Land-based imaging condition / Weighted spectral correlation angle (WSCA)

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