# A Miniaturized Semantic Segmentation Method for Remote Sensing Image

Source: [A Miniaturized Semantic Segmentation Method for Remote Sensing Image](<https://www.sota2.com/research/paper/68fa64f91e209131cea077e5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:15:52.693Z

Authors: Shou-Yu Chen, Guang-Sheng Chen, Wei-Peng Jing

Published: 2018-10-27T05:58:36.000Z

## 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

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1810.11603>)

[PDF](<https://arxiv.org/pdf/1810.11603.pdf>)