High resolution remote sensing satellite is an indispensable and effective tool in the detection of external damage, and the key to realize the identification of external damage signs is the application of change detection technology. In this paper, an object-level change detection method based on optimal scale segmentation and feature optimization is proposed. Firstly, the optimal segmentation scale is determined by scale evaluation parameters. Then, based on the results of the optimal segmentation scale, J-M distance algorithm and XGBoost model were respectively used to optimize the multi-source feature sets, and the appropriate feature sets were screened by comparative analysis. Finally, a support vector machine (SVM) classifier was used to calculate the Euclidean distance between the feature parameters and classify them, and two classification results were obtained, namely, changed and unchanged. The results show that the accuracy indexes of XGBoost model algorithm after feature optimization are all higher than the change detection results of J-M distance algorithm, and the same segmentation scale and feature set are applied to validate the change detection results in images with different resolutions, in the accuracy evaluation based on area and object, F1 scores reached 83.31% and 84.09% respectively. Meanwhile the accuracy rate exceeded 79% and the recall rate exceeded 86%, which indicates that the algorithm used in this paper can obtain higher detection accuracy under different resolutions, and the manual intervention in feature selection and feature set establishment rules is reduced, which provides effective technical support for change detection along the cable.