A Miniaturized Semantic Segmentation Method for Remote Sensing Image

October 27, 2018 · Entered Twilight · 🏛 arXiv.org

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Repo contents: .idea, Metrics, Models, README.md, __init__.py, _test_read_data.py, _test_utils.py, config.py, read_data.py, train.py, utils.py

Authors Shou-Yu Chen, Guang-Sheng Chen, Wei-Peng Jing arXiv ID 1810.11603 Category cs.CV: Computer Vision Cross-listed cs.AI Citations 1 Venue arXiv.org Repository https://github.com/Isnot2bad/Micro-Net Last Checked 2 months ago

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. We provide a Keras and Tensorflow hybrid programming implementation for our model: https://github.com/Isnot2bad/Micro-Net