Hyperspectral image target detection based on exponential smoothing method

In this paper, we proposed a new hyperspectral image target detector based on time series analysis, named as Exponential Smoothing Target Detector (ES-TD). As a classical method of time series analysis, the exponential smoothing method can choose the motional weight in the different part of data, which accelerates the reconstruction and forecast of the unknown data. The proposed method has a three-step process. Firstly, we select the applicable smoothing parameter according to the shape of the data curve. Then, given the reference and test spectral curves, we use the exponential smoothing method to obtain two new smoothing curves. Finally, we calculate the similarity between the two smoothing curves using SAM to determine whether the test spectral curve is the target or not. The proposed method has the feature of high computational efficiency and robustness. Experimental results on two real hyperspectral data sets demonstrate the advantages of the new method.