Remote sensing image change detection has resulted in great breakthroughs in the field of land cover observations. However， the noise of remote sensing image will impact the performance of the change detection methods. To improve the accuracy of change detection， a change detection method based on the Siamese multi-scale attention network （SMA-Net） has been proposed. Firstly， we combine atrous convolutional layers with different dilated rates and spatial attention module to get the multi-scale feature extraction module. Then， the feature maps on the same layer are subtracted to get the difference feature maps and the channel attention mechanism is used to enhance the feature extraction effect. Finally， the change detection result is output by fully connection layers. The proposed method is compared with other change detection methods on the original remote sensing image data with or without noise data. The experimental result shows that the change detection method which uses the spectral information of a single pixel as input， like support vector machine method， is susceptible to the image noise， and the convolutional neural network （CNN） based method is much less susceptible to the image noise. The proposed SMA-Net outperforms other methods on the accuracy and is less susceptible to the image noise.