Coastal wetlands are dynamic and fragile ecosystems, and they have taken place obvious changes, which affected by siltation and erosion, coastal development and utilization, therefore it is of great practical significance to timely monitor coastal wetlands changes. Remote sensing change detection technology can obtain the changes occurred in different times by mathematical model analysis, so it provides an effective way to monitor the dynamic changes of coastal wetlands. From the perspective of analysis unit of remote sensing change detection technology, change detection methods can be divided into pixel-based change detection methods and object-based change detection methods. Pixel-based change detection methods are sensitive to image registration errors, and their salt-and-pepper phenomena are also serious, while object-based methods are affected by image segmentation parameters, and often complicated for users. In order to solve the problems above, saliency-guided change detection combining pixel-based and object-based methods is proposed, in which the scene characteristics of coastal wetlands are taken into account. Firstly, the brightness, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) features are extracted, and the feature difference images are then obtained. Secondly, the Maximum Symmetric Surround (MSS) saliency detection algorithm is used to generate the saliency maps of feature difference images, and then the combination of Fuzzy C-means (FCM) with Markov Random Field (MRF) is used to extract the initial change detection result at the pixel level. Finally, multi-scale segmentation algorithm is utilized for object-oriented image segmentation, in which Rate of Change of Local Variance (ROC-LV) is used to estimate the optimal segmentation scales. The uncertainty index of segmentation objects is constructed to adaptively select training samples, and these training samples are used to train random forest classifier which is used to obtain the final change detection results. The experiments are carried out using Ziyuan-3 images in Yancheng coastal wetlands, Jiangsu Province, the results show that the proposed saliency-guided change detection combining pixel-based and object-based methods obtains the best change detection result when the segmentation scale and the uncertainty threshold are 55 and 0.7 respectively, the proposed method obtains the highest overall accuracy and accuracy ratio compared with traditional pixel-based, object-based, SG-PCAK, and SG-RCVA-RF methods, overall accuracy of our proposed method is 93.51%, which is higher than SG-PCAK method of 5.95%, false rate is reduced by 35.96% and accuracy ratio is improved by 29.24%, compared with SG-PCAK method. False rate is reduced by 29.04% and 22.78%, compared with the pixel-based method and object-based method respectively. Accuracy ratio of our proposed method is improved by 14.23%, compared with SG-RCVA-RF method. Therefore, the experimental results demonstrate the proposed change detection method improves the accuracy of monitoring coastal wetlands changes, compared with traditional change detection methods.