  In order to save the memory, we propose a miniaturization method for neural
network to reduce the parameter quantity existed in remote sensing (RS) image
semantic segmentation model. The compact convolution optimization method is
first used for standard U-Net to reduce the weights quantity. With the purpose
of decreasing model performance loss caused by miniaturization and based on the
characteristics of remote sensing image, fewer down-samplings and improved
cascade atrous convolution are then used to improve the performance of the
miniaturized U-Net. Compared with U-Net, our proposed Micro-Net not only
achieves 29.26 times model compression, but also basically maintains the
performance unchanged on the public dataset. We provide a Keras and Tensorflow
hybrid programming implementation for our model:
https://github.com/Isnot2bad/Micro-Net
