Considering the reconstruction error of autoencoder can reflect the characteristic of anomalies, this paper presents a novel hyperpsectral anomaly detection algorithm utilizes the residual of reconstruction error to estimate the anomalous information of the image. We firstly employ an adaptive dual concentric window to collect the training sets, and input pixels in the whole window and the outer window into the sparse autoencoders to learn the representations of the input, respectively. Then we use the representation parameters to reconstruct the test pixel and calculate the detection result by a deviation of reconstruction errors under two cases. The proposed sparse autoencoder-based anomaly detector experimental results have been conducted into the San Diego airport dataset and the Urban area dataset, the detection performances verified by the receiver operating characteristic curve and the area under curve show that the proposed method outperforms other representative detection methods.