Abstract: In order to make better use of spatial and spectral information in hyperspectral imaging, a sparse representation model was presented based on high spectral imaging target detection method in this paper. First, by extracting the over-complete dictionary through training samples, the sparse representation model of remote sensing image sparse expression was established for dimensionality reduction purposes. And the main information of remote sensing image was presented. Then, the hyperspectral remote sensing image target detection (SRM-TD) based on the sparse expression model was used to detect the hyperspectral remote sensing image by using the traditional target detector combined with the target known spectral information. The experimental results of the three kinds of image data show that the optimal detection result can be obtained by setting the degree of sparse L under the number of iterations. The proposed detection method is superior to the traditional high spectral imaging target detection method in the parameter setting, selecting and operating results.