Restricted by spatial resolution and detector level,traditional hyperspectral image target detection algorithms focus more on quantitative processing based on spectral analysis. In recent years,with the development of ground and near-ground imaging platforms and spectral imaging technology,land-based hyperspectral images have realized the unification of high spatial and spectral resolutions. Compared with hyperspectral remote sensing images,land-based hyperspectral images have a higher spatial resolution,and their targets are characterized by rich details and large scales so that the geometric shape information and fine spectral information of the targets can be utilized in the target detection task at the same time. Constrained energy minimization (CEM) is a classical target detection algorithm for hyperspectral images,which is suitable for the case that specific components account for a small proportion of the total variance of the image,highlighting certain target information to be detected and suppressing the background information,to achieve the effect of separating the target to be detected from the image. However,CEM is sensitive to the targets scale,and the algorithms detection effect decreases significantly as the number of target pixels increases. This problem is because CEM is based on the assumption that the target spectral information is excluded from the statistical background. Still,it is difficult to exclude the target spectral information in advance. Instead,it directly counts the spectra of each pixel of the full-domain image to approximate instead of the background spectra. To solve the problem that CEM is ineffective in the detection task of larger targets and to improve the algorithms target detection capability in land-based hyperspectral images,this paper proposes a CEM method based on spatial inspection guidance (Space inspection guidance CEM,SIG-CEM). The method first analyzes the acquired hyperspectral images to be measured by principal component analysis,feeds the first principal component image into the spatial target detection model,and frames the target using the coordinate information obtained from the detection results. Then,the image elements containing the target in the framed region are removed when the autocorrelation matrix in the CEM is obtained,thus effectively reducing the suppression of the target. The experiments using publicly available remotely sensed hyperspectral images and measured land-based hyperspectral images show that the SIG-CEM algorithm can avoid the influence of the traditional CEM algorithm in which the target signal participates in the operation as a background signal on the detection results. In the experiments on the public dataset,compared with other traditional target detection algorithms,the AUC value of the SIG-CEM algorithm reaches 0.973 7,which effectively improves the accuracy of target detection; in the experiments on the measured land-based hyperspectral image data,the AUC value of the SIG-CEM is improved by an average of 0.055 compared with that of the CEM. At the same time,the experiments,to a certain extent,verify that the SIG-CEM algorithm has strong robustness and applicability for different types of hyperspectral images. This study proposes a target detection method specifically for land-based hyperspectral images,which promotes the development and application of land-based hyperspectral images in target localization and identification in the future.