A Miniaturized Semantic Segmentation Method for Remote Sensing Image

Autor:	Chen, Shou-Yu, Chen, Guang-Sheng, Jing, Wei-Peng
Rok vydání:	2018
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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 Comment: 5 pages, 3 figures, 3 tables, this paper is to be submitted to the conference
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1810.11603 Zobrazit plný text záznamu View this record from Arxiv