[A hyperspectral subpixel target detection method based on inverse least squares method]. 
In the present paper, an inverse least square (ILS) method combined with the Mahalanobis distance outlier detection method is discussed to detect the subpixel target from the hyperspectral image.  Firstly, the inverse model for the target spectrum and all the pixel spectra was established, in which the accurate target spectrum was obtained previously, and then the SNV algorithm was employed to preprocess each original pixel spectra separately.  After the pretreatment, the regressive coefficient of ILS was calculated with partial least square (PLS) algorithm.  Each point in the vector of regressive coefficient corresponds to a pixel in the image.  The Mahalanobis distance was calculated with each point in the regressive coefficient vector.  Because Mahalanobis distance stands for the extent to which samples deviate from the total population, the point with Mahalanobis distance larger than the 3sigma was regarded as the subpixel target.  In this algorithm, no other prior information such as representative background spectrum or modeling of background is required, and only the target spectrum is needed.  In addition, the result of the detection is insensitive to the complexity of background.  This method was applied to AVIRIS remote sensing data.  For this simulation experiment, AVIRIS remote sensing data was free downloaded from the NASA official websit, the spectrum of a ground object in the AVIRIS hyperspectral image was picked up as the target spectrum, and the subpixel target was simulated though a linear mixed method.  The comparison of the subpixel detection result of the method mentioned above with that of orthogonal subspace projection method (OSP) was performed.  The result shows that the performance of the ILS method is better than the traditional OSP method.  The ROC (receive operating characteristic curve) and SNR were calculated, which indicates that the ILS method possesses higher detection accuracy and less computing time than the OSP algorithm.