In view of the irregularity of the geometric features of the scattered targets in remote sensing images, a geometric feature extraction method for remote sensing images based on irregular mark point process is proposed. Firstly, the image is divided into target and background, and the distribution and geometric features of the target are established by using the irregular marking point process. The random point are used to determine the target position, and the marks associating individual points are defined with a set of nodes to depict the geometric shape of the target. Then, on this basis, the multi-Gaussian distribution and Kullback-Leibler divergence are used to define the energy function and the heterogeneous energy function, and form the global energy function. The global energy function is converted to the probability distribution function by using the unconstrained Gibbs function, and the appropriate Metropolis-Hastings sampling algorithm is designed under the maximization probability distribution function criterion. The optimal target extraction result is obtained. Finally, the proposed method is used to extract the scattered targets from remote sensing images, it can be seen from the experimental results that the proposed method achieves better experimental results. The traditional mark point process method is extended to the irregular mark point process. According to the experimental results of remote sensing image extraction, it can be seen that the method can be more accurate to fit the scattered targets.