Image deblurring has the goal of restoring a sharp image from a degraded one. Currently, most deblurring methods are designed for natural images; these methods may not perform well when applied to remote sensing images. There are many differences between remote sensing and natural images, e.g., shooting distance, content complexity, and clarity. Therefore, a blind motion deblurring method specifically designed for remote sensing images called dual scale parallel spatial fusion network (DSPF-Net) is proposed. It has three innovative aspects: a dual-scale connection module is added between the two scales of the bottleneck layer and the decoder to realize the fusion of spatial detail and semantic features. Second, an adaptive spatial selection module is designed, which adds the function of selecting global and local spatial features. Finally, the cross-scale fusion (CSF) module is designed to restore the edge details and main structures by fusing the multi-scale features between the encoder and decoder. Extensive experiments are established on the synthetic dataset Blur-RS, the averaged peak signal-to-noise ratio and structural similarity are improved by 0.7916% and 0.0265%, respectively, compared to the best-performing comparison method. It shows that DSPF-Net has advantages in the task of blind motion deblurring of remote sensing images.