Sparse representation had achieved very good results in hyperspectral imaging anomaly detections. A local joint sparse index method was employed, which combined local spectral sparse index and local spatial sparse index. The influence of the window design on the detection results was discussed. The algorithm combining the adaptive subspace decomposition and the detection method based on local joint sparse index was proposed to improve the detection effect. With synthetic and real hyperspectral imaging datasets in the simulation experiment, the results show that the algorithms utilizing the new models could improve the effectiveness of the detection results to a certain degree, and different window designs have an impact on the results.