Aiming at the prominent "pseudo-change" problem in the detection of changes in heterogeneous high-resolution remote sensing images, this paper proposes an object-level change detection method based on improved bilateral filtering and small targets suppression model. On the grounds of the traditional filtering strategy based on global pixels, this paper designs an improved bilateral filter under the boundary constraint of segmented objects to improve the spatial structure consistency between pixels in the object; Moreover, in order to further weaken the "false change" caused by local outliers, the paper proposes a small target suppression strategy based on high-order neuron on-off channel; Finally, the Otsu method is used to classify the difference information and obtain the final change detection results. The experimental results of multiple groups of heterogeneous high-resolution remote sensing images show that the proposed method can effectively reduce the detection error caused by "pseudo change", the overall accuracy can reach 92.2%, and the false detection rate is less than 8.7%. It is significantly better than the three groups of comparison methods in visual analysis and quantitative evaluation.