For the problem of insufficient accuracy of traditional fuzzy C-means clustering （FCM） algorithm for noise-containing multi-spectral remote sensing image segmentation， an FCM multi-spectral remote sensing image segmentation algorithm combining adaptive local fuzzy subspace and enhanced KL is proposed. Firstly， the local fuzzy factor is used to automatically eliminate the noise interference and extract the local spatial information of the image by similarity metric and adaptive constraint parameters without relying on parameters. Secondly， the original image information and the local spatial information processed by the fuzzy factor are unified and integrated into the fuzzy subspace clustering， and the multiple channels of the image are adaptively weighted to enhance the segmentation accuracy. Finally， the KL information is introduced into the FCM objective function in the form of regular terms for clustering calculation， and the outliers in the membership matrix are removed by ESD （Extreme Studentized Deviate） detection model to enhance the KL prior information and reduce the ambiguity of the membership. The experiments of real multi-spectral remote sensing image segmentation show that in the simulation of noise environments， the algorithm in this paper can suppress the noise and can guarantee the segmentation accuracy better at the same time. In addition， the algorithm in this paper outperforms several other variant FCM algorithms in terms of evaluation indexes such as segmentation accuracy， fuzzy coefficient， and peak signal-to-noise ratio.