Objectives  Remote sensing image segmentation is one of the key steps in the remote sensing image analysis progress. Due to the influence of illumination and other cases, the high-resolution remote sensing images show weak edges between different features, which is of great disadvantages to the subsequent image segmentation. The low⁃dimensional texture feature operator combined with simple linear iterative clustering (SLIC) and the support vector machine (SVM) optimized by double-mutant butterfly optimization algorithm (DBOA) are proposed to solve the problem.

  Methods  Firstly, the texture extraction operator called low-dimensional completed local ternary pattern (LCLTP) suitable for remote sensing image is proposed, which is based on completed local ternary pattern (CLTP) and has less dimension compared with CLTP. Meanwhile, because of its robustness to illumination, LCLTP can identify weak edges very well. Secondly, we introduce LCLTP into SLIC to perform initial super-pixel segmentation and combine the feature of LCLTP and SLIC as the comprehensive feature. And DBOA is used to optimize the parameter selection of SVM. Finally, the trained SVM model is used to classify the super-pixels and the final segmentation image is obtained.

  Results  Compared with traditional algorithms, the proposed method is more sensitive to the weak edges and more robust to independent noises. It performs better segmentation to pivotal objectives such as buildings, plants and roads. Compared with convolutional neural network, the boundary recall (BR) values of the proposed method are increased by 1.9% and 2.33% on dataset-1 and dataset-2, respectively. Kappa coefficient are increased by 0.036 and 0.027, respectively.

  Conclusions  The proposed method has better generalization ability with higher segmentation accuracy for the weak edge by combining LCLTP and SLIC, meaning that, the comprehensive features can distinguish different objects more effectively. By taking the comprehensive features as the input of SVM, the accuracy of classification is improved and the time of sample production is saved. DBOA is used to optimize the parameter selection process of SVM, which improves the parameter selection precision and the running efficiency.