With the rapid development of earth observation technology， high-resolution remote sensing image change detection has become a research hotspot in the remote sensing domain. The increase in spatial resolution brings rich spatial information， but also leads to the problem of "pseudo-change" caused by the change of the spectrum and other performance characteristics， which does not change due to phenological differences. Morphological Attribute Profiles （MAPs）， as an efficient spatial information modeling method， can accurately describe complex change characteristics from different attributes and multiple scales， and have been widely used in the field of change detection tasks. Nevertheless， the existing MAPs methods usually do not consider the properties and scale balance of the differential profile， so they are prone to fall into local optimum； at the same time， the effective fusion of differential features into change detection results is another difficult problem faced by such methods. To this end， this paper proposes a change detection method that combines adaptive MAPs with decision fusion. Firstly， the initial differential feature set is extracted by CVA on the MAPs； On this basis， a Balanced Optimal Objective Function （BOF） is designed to extract the optimal differential feature set； Finally， based on the proposed change intensity evidence index （EVI） and evidence confidence index （IOEC）， a multi-feature decision fusion framework is constructed to obtain change detection results. The experimental results show that the Overall Accuracy （OA） and F1 score （F1） of the proposed method can reach 96.41% and 88.67%， respectively. which are significantly better than the comparison methods in both visual analysis and quantitative evaluation. especially for the "pseudo-variation" proposed in this paper. Compared with the comparison method， the method in this paper can realize more accurate discrimination and effectively alleviate the "pseudo change".