A Miniaturized Semantic Segmentation Method for Remote Sensing Image

ABSTRACT 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. Index Terms--semantic segmentation, compact convolution, atrous convolution, deep learning 1. INTRODUCTION As the major data source in mapping [1], earth observation [2], ground target recognition [3], RS images have important research value.