# Deep learning for semantic segmentation of remote sensing images with rich spectral content

arXiv: https://arxiv.org/abs/1712.01600 · HTML: https://mlsanity.com/paper/1712.01600/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2017.

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

- [A High Resolution Urban and Rural Settlement Map of Africa Using Deep Learning and Satellite Imagery](https://mlsanity.com/paper/2411.02935/) (2024)
- [Evaluation of Dirichlet Process Gaussian Mixtures for Segmentation on Noisy Hyperspectral Images](https://mlsanity.com/paper/2203.02820/) (2022)

## Abstract (arXiv)

With the rapid development of Remote Sensing acquisition techniques, there is a need to scale and improve processing tools to cope with the observed increase of both data volume and richness. Among popular techniques in remote sensing, Deep Learning gains increasing interest but depends on the quality of the training data. Therefore, this paper presents recent Deep Learning approaches for fine or coarse land cover semantic segmentation estimation. Various 2D architectures are tested and a new 3D model is introduced in order to jointly process the spatial and spectral dimensions of the data. Such a set of networks enables the comparison of the different spectral fusion schemes. Besides, we also assess the use of a " noisy ground truth " (i.e. outdated and low spatial resolution labels) for training and testing the networks.