# A Comparison and Strategy of Semantic Segmentation on Remote Sensing Images

arXiv: https://arxiv.org/abs/1905.10231 · HTML: https://mlsanity.com/paper/1905.10231/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2019.

## Built on by (most cited first, 2 of 2)

- [Building Segmentation on Satellite Images and Performance of Post-Processing Methods](https://mlsanity.com/paper/2212.13712/) (2022)
- [Evaluation of Deep Learning Semantic Segmentation for Land Cover Mapping on Multispectral, Hyperspectral and High Spatial Aerial Imagery](https://mlsanity.com/paper/2406.14220/) (2024)

## Abstract (arXiv)

In recent years, with the development of aerospace technology, we use more and more images captured by satellites to obtain information. But a large number of useless raw images, limited data storage resource and poor transmission capability on satellites hinder our use of valuable images. Therefore, it is necessary to deploy an on-orbit semantic segmentation model to filter out useless images before data transmission. In this paper, we present a detailed comparison on the recent deep learning models. Considering the computing environment of satellites, we compare methods from accuracy, parameters and resource consumption on the same public dataset. And we also analyze the relation between them. Based on experimental results, we further propose a viable on-orbit semantic segmentation strategy. It will be deployed on the TianZhi-2 satellite which supports deep learning methods and will be lunched soon.